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Traffic sign detection plays an important role in improving the capabilities of automated driving systems by addressing road safety challenges in sustainable urban living. In this paper, we present DSRA-DETR, a novel approach focused on improving multiscale detection performance. Our approach integrates the dilated spatial pyramid pooling model (DSPP) and the multiscale feature residual aggregation module (FRAM) to aggregate features at various scales. These modules excel at reducing feature noise and minimizing loss of low-level features during feature map extraction. Additionally, they enhance the model’s capability to detect objects at different scales, thereby improving the accuracy and robustness of traffic sign detection. We evaluate the performance of our method on two widely used datasets, the GTSDB and CCTSDB, and achieve impressive average accuracies (APs) of 76.13% and 78.24%, respectively. Compared with other well-known algorithms, our method shows a significant improvement in detection accuracy, demonstrating its superiority and generality. Our proposed method shows great potential for improving the performance of traffic sign detection for autonomous driving systems and will help in the development of safe and efficient autonomous driving technologies.

The ability to sense and understanding the driving environment is a key technology for ADAS and autonomous driving. Human drivers have to pay more visual attention to important or target elements and ignore unnecessary ones present in their field of sight. A model that computes this visual attention of targets in a specific driving environment is essential and useful in supporting autonomous driving, object-specific tracking & detection, driving training, car collision warning, traffic sign detection, etc. In this paper, we propose a new framework of visual attention that can predict important objects in the driving scene using a conditional generative adversarial network. A large scale Visual Attention Driving Database (VADD) of saliency heat-maps is built from existing driving datasets using a saliency mechanism. The proposed framework model takes its strength from these saliency heat-maps as conditioning label variables. The results show that the proposed approach makes us able to predict heat-maps of most important objects in a driving environment.

Object detection and image recognition are some of the most significant and challenging branches in the field of computer vision. The prosperous development of unmanned driving technology has made the detection and recognition of traffic signs crucial. Affected by diverse factors such as light, the presence of small objects, and complicated backgrounds, the results of traditional traffic sign detection technology are not satisfactory. To solve this problem, this paper proposes two novel traffic sign detection models, called YOLOv5-DH and YOLOv5-TDHSA, based on the YOLOv5s model with the following improvements (YOLOv5-DH uses only the second improvement): (1) replacing the last layer of the ‘Conv + Batch Normalization + SiLU’ (CBS) structure in the YOLOv5s backbone with a transformer self-attention module (T in the YOLOv5-TDHSA’s name), and also adding a similar module to the last layer of its neck, so that the image information can be used more comprehensively, (2) replacing the YOLOv5s coupled head with a decoupled head (DH in both models’ names) so as to increase the detection accuracy and speed up the convergence, and (3) adding a small-object detection layer (S in the YOLOv5-TDHSA’s name) and an adaptive anchor (A in the YOLOv5-TDHSA’s name) to the YOLOv5s neck to improve the detection of small objects. Based on experiments conducted on two public datasets, it is demonstrated that both proposed models perform better than the original YOLOv5s model and three other state-of-the-art models (Faster R-CNN, YOLOv4-Tiny, and YOLOv5n) in terms of the mean accuracy (mAP) and F1 score, achieving mAP values of 77.9% and 83.4% and F1 score values of 0.767 and 0.811 on the TT100K dataset, and mAP values of 68.1% and 69.8% and F1 score values of 0.71 and 0.72 on the CCTSDB2021 dataset, respectively, for YOLOv5-DH and YOLOv5-TDHSA. This was achieved, however, at the expense of both proposed models having a bigger size, greater number of parameters, and slower processing speed than YOLOv5s, YOLOv4-Tiny and YOLOv5n, surpassing only Faster R-CNN in this regard. The results also confirmed that the incorporation of the T and SA improvements into YOLOv5s leads to further enhancement, represented by the YOLOv5-TDHSA model, which is superior to the other proposed model, YOLOv5-DH, which avails of only one YOLOv5s improvement (i.e., DH).

In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which we termed the feature-selection-based attentionaldeconvolution detector (FSADD), is used along with the “you look only once” (YOLO) v5 structure for feature selection. When applying feature selection inside a detection algorithm, the network divides the extracted feature maps after the convolution layer into similar and non similar feature maps. Generally, the feature maps obtained after the convolution layers are the outputs of filters with random weights. Owing to the randomness of the filter, the network obtains various kinds of feature maps with unnecessary components, which degrades the detection performance. However, grouping feature maps with high similarities can increase the relativeness of each feature map, thereby improving the network detection of specific targets from images. Furthermore, the proposed FSADD model has modified sizes of the receptive fields for improved traffic sign detection performance. Many of the available general detection algorithms are unsuitable for the German traffic sign detection benchmark (GTSDB) because of the small sizes of these signs in the images. Experimental comparisons were performed with respect to the GTSDB to show that the proposed FSADD is comparable to the state-of-the-art while detecting 29 kinds of traffic signs with 73.9% accuracy of classification performances.

Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited computational capacity, which cannot meet the direct needs of autonomous vehicles for detection model performance and efficiency. To address the above concerns, this paper proposes an improved SEDG-Yolov5 traffic sign detection method based on knowledge distillation. Firstly, the Slicing Aided Hyper Inference method is used as a local offline data augmentation method for the model training. Secondly, to solve the problems of high-dimensional feature information loss and high model complexity, the inverted residual structure ESGBlock with a fused attention mechanism is proposed, and a lightweight feature extraction backbone network is constructed based on it, while we introduce the GSConv in the feature fusion layer to reduce the computational complexity of the model further. Eventually, an improved response-based objectness scaled knowledge distillation method is proposed to retrain the traffic sign detection model to compensate for the degradation of detection accuracy due to light-weighting. Extensive experiments on two challenging traffic sign datasets show that our proposed method has a good balance on detection precision and detection speed with 2.77M parametric quantities. Furthermore, the inference speed of our method achieves 370 FPS with TensorRT and 35.6 FPS with ONNX at FP16-precision, which satisfies the requirements for real-time sign detection and edge deployment.

Object detection and image recognition are some of the most significant and challenging branches in the field of computer vision. The prosperous development of unmanned driving technology has made the detection and recognition of traffic signs crucial. Affected by diverse factors such as light, the presence of small objects, and complicated backgrounds, the results of traditional traffic sign detection technology are not satisfactory. To solve this problem, this paper proposes two novel traffic sign detection models, called YOLOv5-DH and YOLOv5-TDHSA, based on the YOLOv5s model with the following improvements (YOLOv5-DH uses only the second improvement): (1) replacing the last layer of the ‘Conv + Batch Normalization + SiLU’ (CBS) structure in the YOLOv5s backbone with a transformer self-attention module (T in the YOLOv5-TDHSA’s name), and also adding a similar module to the last layer of its neck, so that the image information can be used more comprehensively, (2) replacing the YOLOv5s coupled head with a decoupled head (DH in both models’ names) so as to increase the detection accuracy and speed up the convergence, and (3) adding a small-object detection layer (S in the YOLOv5-TDHSA’s name) and an adaptive anchor (A in the YOLOv5-TDHSA’s name) to the YOLOv5s neck to improve the detection of small objects. Based on experiments conducted on two public datasets, it is demonstrated that both proposed models perform better than the original YOLOv5s model and three other state-of-the-art models (Faster R-CNN, YOLOv4-Tiny, and YOLOv5n) in terms of the mean accuracy (mAP) and F1 score, achieving mAP values of 77.9% and 83.4% and F1 score values of 0.767 and 0.811 on the TT100K dataset, and mAP values of 68.1% and 69.8% and F1 score values of 0.71 and 0.72 on the CCTSDB2021 dataset, respectively, for YOLOv5-DH and YOLOv5-TDHSA. This was achieved, however, at the expense of both proposed models having a bigger size, greater number of parameters, and slower processing speed than YOLOv5s, YOLOv4-Tiny and YOLOv5n, surpassing only Faster R-CNN in this regard. The results also confirmed that the incorporation of the T and SA improvements into YOLOv5s leads to further enhancement, represented by the YOLOv5-TDHSA model, which is superior to the other proposed model, YOLOv5-DH, which avails of only one YOLOv5s improvement (i.e., DH).

In today's world, nearly everything we have a tendency to do has been simplified by machine-driven tasks. In a trial to specialize in the road whereas driving, drivers usually miss out on signs on the facet of the road, that can be dangerous for them and for the folks around them. This drawback may be avoided if there was AN economical thanks to inform the motive force while not having them to shift their focus. Traffic Sign Detection and Recognition (TSDR) plays a vital role here by detection and recognizing a symptom, therefore notifying the motive force of any coming signs. This not solely ensures road safety, however additionally permits the motive force to be at very little a lot of ease whereas driving on tough or new roads. Another normally long-faced drawback isn't having the ability to know the which means of the sign. With the assistance of this Advanced Driver help Systems (ADAS) application, drivers can not face the matter of understanding what the sign says. during this paper, we have a tendency to propose a way for Traffic Sign Detection and Recognition exploitation image process for the detection of a symptom and a Convolutional Neural Networks (CNN) for the popularity of the sign. CNNs have a high recognition rate, therefore creating it fascinating to use for implementing varied laptop vision tasks. TensorFlow is employed for the implementation of the CNN. Keywords: actitvity recognition; knowledge collection; knowledge preprocessing; coaching CNN model ;evaluating model; predicting the result.

The detection of traffic signs in clean and noise-free images has been investigated by numerous researchers; however, very few of these works have focused on noisy environments. While in the real world, for different reasons (e.g. the speed and acceleration of a vehicle and the roughness around it), the input images of the convolutional neural networks (CNNs) could be extremely noisy. Contrary to other research works, in this paper, we investigate the robustness of the deep learning models against the synthetically modeled noises in the detection of small objects. To this end, the state-of-the-art architectures of Faster-RCNN Resnet101, R-FCN Resnet101, and Faster-RCNN Inception Resnet V2 are trained by means of the Tsinghua-Tencent 100K database, and the performances of the trained models on noisy data are evaluated. After verifying the robustness of these models, different training scenarios (1 – Modeling various climatic conditions, 2 – Style randomization, and 3 – Augmix augmentation) are used to enhance the model robustness. The findings indicate that these scenarios result in up to 13.09%, 12%, and 13.61% gains in the mentioned three networks by means of the mPC metric. They also result in 11.74%, 8.89%, and 7.27% gains in the rPC metric, demonstrating that improvement in robustness does not lead to performance drop on the clean data.

Traffic sign detection is an important research direction in computer vision, which is of great significance for autonomous driving and advanced assisted driving systems. Due to the complexity of traffic signs in natural scenes, existing traffic sign detection algorithms have disadvantages such as high false detection rates and poor robustness. To improve the accuracy of traffic sign detection, a feature expression enhanced SSD(ESSD) detection algorithm is proposed. ESSD extracts semantic information in a lightweight way, adds detailed information for fusion, and forms a new feature map through multiple convolution operations to enhance feature expression. Meanwhile, a new target default box was designed to increase the focus on traffic signs. The SSD and ESSD were retrained on TT100K and CCTSDB datasets. Experimental results show that the mAP of the improved ESSD is 81.26% and 90.52% and can improve AP up to 40%. The robustness of the ESSD model was verified using the PASCAL VOC data set, which showed better detection of small objects.

With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and mAP@0.5. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% mAP@0.5 in TT100K, which exceeds the benchmark network by 4.9%.

The traffic sign detection algorithm based on Faster Region-Based Convolutional Neural Network (R-CNN) has been applied to various intelligent-vehicles driving scenarios. However, the model of the current detection algorithm has certain shortcomings, which include the influence of weather and light, the detection of distance traffic signs, and the detection of similar traffic signs. To solve these problems, this paper proposes an improved traffic sign detection method based on Faster R-CNN. First, we propose a fusion method that fuses the feature pyramid into the Faster R-CNN algorithm. This fusion method can extract object features with precision and decrease the influence of weather and light. Second, a deformable convolution (DCN) which can train the algorithm to identify traffic signs with precision and make similar signs more distinguishable, and in particular make it work better with distorted images, is added to the backbone network. Lastly, we apply ROI align to replace the ROI pooling, which can avoid the distant traffic sign detail loss caused by pooling and increase the detection precision of distant traffic signs. The experimental results on both the TT100k dataset and real intelligent vehicle tests demonstrate that the algorithm is superior to the original Faster R-CNN algorithm and four other state-of-the-art methods in traffic sign detection, specifically in small-target traffic sign detection and low-intensity environments such as sunset time and rainy days. Therefore, the proposed method is helpful to improve the traffic sign detection performance in extreme environments (low-light intensity or rainy weather).

Traffic signs detection and recognition is an essential and challenging task for driverless cars. However, the detection of traffic signs in most scenarios belongs to small target detection, and most existing object detection methods show poor performance in these cases, which increases the difficulty of detection. To further improve the accuracy of small object detection for traffic signs, this paper proposed an optimization strategy based on the YOLOv4 network. Firstly, an improved triplet attention mechanism was added to the backbone network. It was combined with optimized weights to make the network focus more on the acquisition of channel and spatial features. Secondly, a bidirectional feature pyramid network (BiFPN) was used in the neck network to enhance feature fusion, which can effectively improve the feature perception field of small objects. The improved model and some state-of-the-art (SOTA) methods were compared on the joint dataset TT100K-COCO. Experimental results show that the enhanced network can achieve 60.4% mAP(Mean Average Precision), surpassing the YOLOv4 by 8% with the same input size. With a larger input size, it can achieve a best performance capability of 66.4% mAP. This work provides a reference for research on obtaining higher accuracy for traffic sign detection in autonomous driving.

Object detection in the process of driving is a convenient and efficient task. However, due to the complex transformation of the road environment and vehicle speed, the scale of the target will not only change significantly but also be accompanied by the phenomenon of motion blur, which will have a significant impact on the detection accuracy. In practical application scenarios, it is difficult for traditional methods to simultaneously take into account the need for real-time detection and high accuracy. To address the above problems, this study proposes an improved network based on YOLOv5, taking traffic signs and road cracks as detection objects and conducting separate research. This paper proposes a GS-FPN structure to replace the original feature fusion structure for road cracks. This structure integrates the convolutional block attention model (CBAM) based on bidirectional feature pyramid networks (Bi-FPN) and introduces a new lightweight convolution module (GSConv) to reduce the information loss of the feature map, enhance the expressive ability of the network, and ultimately achieve improved recognition performance. For traffic signs, a four-scale feature detection structure is used to increase the detection scale of shallow layers and improve the recognition accuracy for small targets. In addition, this study has combined various data augmentation methods to improve the robustness of the network. Through experiments using 2164 road crack datasets and 8146 traffic sign datasets made by LabelImg, compared to the baseline model (YOLOv5s), the modified YOLOv5 network improves the mean average precision (mAP) result of the road crack dataset and small targets in the traffic sign dataset by 3% and 12.2%, respectively.

Methods: First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo. Secondly, in order to improve the extraction ability of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) is used, and candidate regions are selected through the ROI detector. Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background. Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy. In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested. Objective: In order to solve the current traffic sign detection problem due to the interference of various complex factors, as it is difficult to effectively carry out the correct detection of traffic signs, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed. Results: The results show that the proposed algorithm has a higher detection accuracy and robustness compared with the current traffic sign recognition technology.

Detection and recognition of urban road traffic signs is an important part of the Modern Intelligent Transportation System (ITS). It is a driver support function which can be used to notify and warn the driver for any possible incidence on the current stretch of road. This paper presents a robust and novel Time Space Relationship Model for high positive urban road traffic sign detection and recognition for a running vehicle. There are three main contributions of the proposed framework. Firstly, it applies fast color-segment algorithm based on color information to extract candidate areas of traffic signs and reduce the computation load. Secondly, it verifies the traffic sign candidate areas to decrease false positives and raise the accuracy by analysing the variation in preceding video-images sequence while implementing the proposed Time Space Relationship Model. Lastly, the classification is done with Support Vector Machine with dataset from real-time detection of TSRM. Experimental results indicate that the accuracy, efficiency, and the robustness of the framework are satisfied on urban road and detect road traffic sign in real time.

Traffic signs are symbols erected on the sides of roads that convey the road instructions to its users. These signs are essential in conveying the instructions related to the movement of traffic in the streets. Automation of driving is essential for efficient navigation free of human errors, which could otherwise lead to accidents and disorganized movement of vehicles in the streets. Traffic sign detection systems provide an important contribution to automation of driving, by helping in efficient navigation through relaying traffic sign instructions to the system users. However, most of the existing techniques have proposed approaches that are mostly capable of detection through static images only. Moreover, to the best of the author’s knowledge, there exists no approach that uses video frames. Therefore, this article proposes a unique automated approach for detection and recognition of Bangladeshi traffic signs from the video frames using Support Vector Machine and Histogram of Oriented Gradient. This system would be immensely useful in the implementation of automated driving systems in Bangladeshi streets. By detecting and recognizing the traffic signs in the streets, the automated driving systems in Bangladesh will be able to effectively navigate the streets. This approach classifies the Bangladeshi traffic signs using Support Vector Machine classifier on the basis of Histogram of Oriented Gradient property. Through image processing techniques such as binarization, contour detection and identifying similarity to circle etc., this article also proposes the actual detection mechanism of traffic signs from the video frames. The proposed approach detects and recognizes traffic signs with 100% precision, 95.83% recall and 96.15% accuracy after running it on 78 Bangladeshi traffic sign videos, which comprise 6 different kinds of Bangladeshi traffic signs. In addition, a public dataset for Bangladeshi traffic signs has been created that can be used for other research purposes.

Traffic sign detection and recognition play an important role in intelligent transportation. In this paper, a traffic sign detection framework based on YOLOv4 is proposed. The original CSPDarkNet53 backbone network model is replaced by RepVGG, and the SPP module is added in the feature pyramid part to improve the expression ability of information. The CCTSDB traffic sign data set is used to detect three categories of indication signs, prohibition signs and warning signs. In order to further improve the performance of YOLOv4 network, K-means++ algorithm was used to perform cluster analysis on the experimental data to determine the size of the priori box suitable for CCTSDB dataset. The experimental results show that the map value of the improved framework is increased by 4.1%, which indicates that the improved YOLOv4 network has a high practical value in traffic sign detection and recognition.

This article presents an algorithm for Ecuadorian regulatory traffic signs detection, under extreme lighting conditions during the day. The method is composed of the following modules, i) video stabilization to reduce vertical oscilation, ii) a method to for obtaining regions of interest (ROIs) based on color information and geometric restrictions, iii) a two-stage multi classification algorithm, based on Extreme Learning Machine (ELM) and HOG descriptor, to classify by form and content. A database of Ecuadors regulatory traffic signs has also been created. It has more than 47,000 images of Ecuadorian road signs divided into 16 classes. The experimental results, from the recognition module, generate an accuracy of 99,85 %, and a sensitivity of 99,78 % in the first stage, and an accuracy of 96,71 % and a sensitivity of 94,16 % in the second stage. In addition, its robustness was compared with two other classifiers, in order to choose the one with the best performance in terms of accuracy and low computational cost. This system works at 24 frames per second, and it was tested in real driving conditions, from 6:00 a.m. until 7:00 p.m., on the streets of several cities and highways of Ecuador.

Lane Detection and Traffic sign detection are the essential components in ADAS .Although there has been significant quantity of analysis dedicated to the detection of lane detection and sign detection in the past, there is still need robustness in the system. An important challenge in the current algorithm is to cope with the bad weather and illumination. In this paper proposes an improved Hough transform algorithm in order to achieve detection of straight line while for the detection of curved sections, the tracking algorithm is studied. The proposed method uses Hybrid KSVD for removing the noise and Hybrid Lane Detection Algorithm is used for identifying the lanes and CNN based approach is used for the Traffic sign Detection. The proposed method offers better Peak Signal to Noise Ratio (PSNR) and Root Mean Square (RMS) in contrast to the existing methods.

Traffic sign detection, though one of the key technologies in intelligent transportation, still has bottleneck in accuracy due to the small size and diversity of traffic signs. To solve this problem, we proposed a two-stage CNN object detection algorithm based on multi-scale feature fusion and prime sample attention. We improved the original Faster R-cnn model in terms of feature extraction and sampling strategy. For feature extraction, to elevate the ability of neural networks to detect small objects, we adopted HRNet as the feature extractor. There are four stages in HRNet - a series of high resolution subnets as the starting point with repeated adding parallel high to low resolution subnets to form other stages. In the whole process, the information in the parallel multi-resolution sub-network is repeatedly exchanged to perform repeated multi-scale fusion. For sampling strategy, we adopted a simple and effective sampling and learning strategy called Prime Sample Attention (PISA), consisting of Importance-based Sample Reweighting (ISR) and Classification Aware Regression Loss (CARL). PISA proposed the concepts of IoU Hierarchical Partial Sorting (IoU-HLR) and Hierarchical Partial Score Sorting (Score-HLR), which sort the importance of positive samples and negative samples in mini-batch respectively. With the proposed method, the training process is focusing on prime samples rather than evenly treat all ones. The algorithm complexity of our method is lower than that of other state-of-the-art. After experiments by TT100K dataset, our method can attain a comparable or even better detection accuracy and robustness.

Traffic sign detection is a critical task in the visual system of the Advanced Driver Assistance System (ADAS) and the Automated Driving System (ADS). Although the general object detection has achieved promising results by using Feature Pyramid Network (FPN) in recent years, we still observed that FPN cannot obtain satisfactory results in traffic sign detection because the size and class distribution of traffic signs are extremely unbalanced. To overcome this problem, a novel Plug-and-Play neck network Integrated Feature Pyramid Network with Feature Aggregation (IFA-FPN) is proposed in this paper based on the statistical characteristics of traffic signs. First, a lightweight operation is introduced to fully utilize the model and improve the inference speed of the model. Second, an Integrated Operation (IO) is introduced to solve the imbalance problem of Region-of-Interests (RoIs) in pyramid levels. Third, we introduce a Feature Aggregation (FA) structure to strengthen the feature representation capacity of feature maps, thereby enhancing the network robustness against the size discrepancy of traffic signs. The experiments are performed on three mainstream datasets, i.e., the German Traffic Sign Detection Benchmark (GTSDB), Swedish Traffic Sign Dataset (STSD), and Tsinghua-Tencent 100k dataset (TT100k). The experimental results demonstrate the superiority of the proposed IFA-FPN in the traffic sign detection tasks. Specifically, when the proposed IFA-FPN is applied to the Cascade RCNN, it achieves 80.3% mAP in GTSDB which surpasses FPN by 9.9%, 65.2% in mAP in STSD which surpasses FPN by 3.5%, and 93.6% in mAP in TT100k which surpasses FPN by 1.6%.

Traffic Sign Detection (TSD) is a complex and fundamental task for developing autonomous vehicles; it is one of the most critical visual perception problems since failing in this task may cause accidents. This task is fundamental in decision-making and involves different internal conditions such as the internal processing system or external conditions such as weather, illumination, and complex backgrounds. At present, several works are focused on the development of algorithms based on deep learning; however, there is no information on a methodology based on descriptive statistical analysis with results from a solid experimental framework, which helps to make decisions to choose the appropriate algorithms and hardware. This work intends to cover that gap. We have implemented some combinations of deep learning models (MobileNet v1 and ResNet50 v1) in a combination of the Single Shot Multibox Detector (SSD) algorithm and the Feature Pyramid Network (FPN) component for TSD in a standardized dataset (LISA), and we have tested it on different hardware architectures (CPU, GPU, TPU, and Embedded System). We propose a methodology and the evaluation method to measure two types of performance. The results show that the use of TPU allows achieving a processing training time 16.3 times faster than GPU and better results in terms of precision detection for one combination.

Road signs are so important because they help preserve safe driving conditions; they also influence the safety of drivers and pedestrians. Without these signs, no one would know the driving speed limit, on which direction to drive down a road, any upcoming hazard, or whether they are approaching a merge. It would be chaotic to drive in such situations. Moreover, these signs help new drivers to find their way in the absence of navigators. Therefore, traffic sign recognition takes a critical place in computer vision applications to develop an effective algorithm. In order to tackle this challenge, we proposed the use of Multi-language Traffic Sign Detection and Classification. One of our contributions in this work is that, instead of using the standard grayscale image, we used the RGB colored image. This image is converted into the 2D highest-level grayscale image using the largest values of each pixel in the RGB channels. The novel generated image has the strongest features of the RGB image that make the features distinct and more informative in the classification step. Consider that, in general, the traffic sign has two colors only, the foreground (text location) and background (non-text location). The Maximally Stable Extremal Regions (MSER) used to extract features from the 2D image where the locations of interest are well-identified exclusively by an extremal property of the intensity function in the location and on its outer boundary. The geometrical properties and thinning operations were used to remove the non-text locations. A multi-language OCR was used to understand multi-language. This proposed method has been tested using 240 images which were collected from the Internet and two datasets. The experimental results demonstrated the performance of the proposed method where the traffic sign detected in 92% of the tested images with a very high percentage of localization.

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.

Traffic sign detection and recognition plays an important part in today’s technology driven world. The purpose of traffic signs is to help drivers as well as pedestrians for safe navigation. The two major phases involved in traffic sign detection and recognition are : identifying the region of interest and proceeding to detect any and all signs that might be present, and further, classifying the detected signs into their respective classes. This paper attempts to review all the existing methods/practices for the detection of signs(real-time).

In recent years, target detection framework based on deep learning has made brilliant achievements. However, real-life traffic sign detection remains a great challenge for most of the state-of-the-art object detection methods. The existing deep learning models are inadequate to effectively extract the features of small traffic signs from large images in real-world conditions. In this paper, we address the small traffic sign detection challenge by proposing a novel small traffic sign detection method based on a highly efficient end-to-end deep network model. The proposed method features fast speed and high precision as it attaches three key insights to the established You Only Look Once (YOLOv3) architecture and other correlated algorithms. Besides, network pruning is appropriately introduced to minimize network redundancy and model size while keeping a competitive detection accuracy. Furtherly, four scale prediction branches are also adopted to significantly enrich the feature maps of multi-scales prediction. In our method, we adjust the loss function to balance the contribution of error source to the total loss. The effectiveness, and robustness of the network is further proved with experiments on Tsinghua-Tencent 100 K traffic sign dataset. The experimental results indicate that our proposed method has achieved better accuracy than that of the original YOLOv3 model. Compared with the schemes in relevant literatures our proposed method not only emerges performance superiors in detection recall and accuracy, but also achieves 1.9–2.7x improvement in detection speed.

Traffic sign detection (TRD) is an essential component of advanced driver-assistance systems and an important part of autonomous vehicles, where the goal is to localize image regions that contain traffic signs. Over the last decade, the amount of research on traffic sign detection and recognition has significantly increased. Although TRD is a built-in feature in modern cars and several methods have been proposed, it is a challenging problem due to the high computational demand, the large number of traffic signs, complex traffic scenes, and occlusions. In addition, it is not clear how can we perform real-time traffic sign detection in embedded systems. In this paper, we focus on image enhancement, which is the first step of many object detection methods. We propose an improved probability-model-based image enhancement method for traffic sign detection. To demonstrate the efficiency of the proposed method, we compared it with other widely used image enhancement approaches in traffic sign detection. The experimental results show that our method increases the performance of object detection. In combination with the Selective Search object proposal algorithm, the average detection accuracies were 98.64% and 99.1% on the GTSDB and Swedish Traffic Signs datasets. In addition, its relatively low computational cost allows for its usage in embedded systems.

As a popular research direction in the field of intelligent transportation, various scholars have widely concerned themselves with traffic sign detection However, there are still some key issues that need to be further solved in order to thoroughly apply related technologies to real scenarios, such as the feature extraction scheme of traffic sign images, the optimal selection of detection methods, and the objective limitations of detection tasks. For the purpose of overcoming these difficulties, this paper proposes a lightweight real-time traffic sign detection integration framework based on YOLO by combining deep learning methods. The framework optimizes the latency concern by reducing the computational overhead of the network, and facilitates information transfer and sharing at diverse levels. While improving the detection efficiency, it ensures a certain degree of generalization and robustness, and enhances the detection performance of traffic signs in objective environments, such as scale and illumination changes. The proposed model is tested and evaluated on real road scene datasets and compared with the current mainstream advanced detection models to verify its effectiveness. In addition, this paper successfully finds a reasonable balance between detection performance and deployment difficulty by effectively reducing the computational cost, which provides a possibility for realistic deployment on edge devices with limited hardware conditions, such as mobile devices and embedded devices. More importantly, the related theories have certain application potential in technology industries such as artificial intelligence or autonomous driving.

The current traffic sign detection technology is disturbed by factors such as illumination changes, weather, and camera angle, which makes it unsatisfactory for traffic sign detection. The traffic sign data set usually contains a large number of small objects, and the scale variance of the object is a huge challenge for traffic indication detection. In response to the above problems, a multi-scale traffic sign detection algorithm based on attention mechanism is proposed. The attention mechanism is composed of channel attention mechanism and spatial attention mechanism. By filtering the background information on redundant contradictions with channel attention mechanism in the network, the information on the network is more accurate, and the performance of the network to recognize the traffic signs is improved. Using spatial attention mechanism, the proposed method pays more attention to the object area in traffic recognition image and suppresses the non-object area or background areas. The model in this paper is validated on the Tsinghua-Tencent 100K data set, and the accuracy of the experiment reached a higher level compared to state-of-the-art approaches in traffic sign detection.

As placement of traffic sign board do not follow any international standard, it may be difficultfor non-local residents to recognize and infer the signs easily. So, this project mainly focuses ondemonstrating a system that can help facilitate this inconvenience. This can be achieved byinterpreting the traffic sign as a voice note in the user’s preferred language. Therefore, the wholeprocess involves detecting the traffic sign, detecting textual data if any with the help of availabledatasets and then processing it into an audio as the output to the user in his/her preferred language.The proposed system not only tackles the above-mentioned problem, but also to an extent ensuressafer driving by reducing accidents through conveying the traffic signs properly. The techniques usedto implement the system include digital image processing, natural language processing and machinelearning concepts. The implementation of the system includesthree major steps which are detection of traffic sign from a captured traffic scene, classification of traffic signs and finally conversion of classified traffic signs to audio message.

In this paper we propose a real-time traffic sign recognition algorithm which is robust to the small-sized objects and can identify all traffic sign categories. Specifically, we present a two-level detection framework which consists of the region proposal module(RPM) which is responsible for locating the objects and the classification module(CM) which aims to classify the located objects. In addition, to solve the problem of insufficient samples, we present an effective data augmentation method based on traffic sign logo to generate enough training data. The experiments are conducted in TT100k, and the results show the superiority of our method.

Traffic sign detection is a very important part of intelligent assisted driving system. However, with the interference of various target sizes, geometric distortion, occlusion and motion blur, fast and accurate detection on large-size car camera image is extremely hard. To achieve both high efficient and accurate detection, we present a traffic sign detection method within a coarse-to-fine framework, which sequentially detects the targets in grid-level and image-level. We demonstrate that focusing first is a more effective detection strategy for small targets in wide detection space. We propose a target grid prediction network, which is a fully convolutional network for binary classification, to realize rapid coarse localization of the target and effectively guide the clipping and scaling of the target area. With the flexible potential target region extracting strategy, the detecting space can be significantly reduced. At the same time, the correctly extracted local areas for the targets can further facilitate the accurate detection of the subsequent traffic sign detector. In the experiments, our method achieves impressive performance in terms of both efficiency and accuracy. On the challenging Tsinghua-Tencent 100K (TT100K) dataset, our method achieves 20.9 FPS detection speed for 1600 × 1600 images and the F1-score of the proposed method for all-scale targets is 91.55.

Much effort has been dedicated to text-based traffic sign detection and recognition. However, there are still two problems. First, unlike English traffic signs with only horizontal text, Chinese traffic signs have both horizontal and vertical text. To the best of our knowledge, there is nothing in the literature about simultaneous recognition of both horizontal and vertical text in Chinese text-based traffic signs. Second, most existing methods focus on wild and expressway scenes; few focus on street scenes. To solve these problems, we propose a mixed vertical-and-horizontal-text traffic sign detection and recognition algorithm for street-level scene. First, an effective combination of different red, green and blue components is used to distinguish the traffic signs from many objects of similar color in the very complex street scenes. Second, unlike English letters, the strokes of many Chinese characters are unconnected, which may result in that a character will be detected as two or more characters. Unlike the English text lines, which are only horizontal, the Chinese text lines on text-based traffic signs are usually both in horizontal and vertical directions. Our proposed method uses the position and structural information of the characters to form the text lines. A dataset of Chinese text-based traffic signs is collected. Experimental results indicate the effectiveness of the proposed method.

Recent trends in the development of autonomous vehicles focus on real-time processing of vast amounts of data from various sensors. The data can be acquired using multiple cameras, lidars, ultrasonic sensors, and radars to collect useful information about the state of the traffic and the surroundings. Significant computational power is required to process the data fast enough, and this is even more pronounced in vehicles that not only assist the driver but are capable of fully autonomous driving. This article proposes speed and accuracy improvement of traffic sign detection and recognition in high-definition images, based on focusing on different regions of interest in traffic images. These regions are determined with efficient and parallelized preprocessing of every traffic image, after which convolutional neural network is applied for detection and recognition in parallel on graphics processing units. We employed different “You Only Look Once” (YOLO) architectures as baseline detectors, due to their speed, straightforward architecture, and high accuracy in general object detection tasks. Several preprocessing procedures were proposed, to achieve real-time performance requirement. Our experiments using a large-scale traffic sign dataset show that we can achieve real-time detection in high-definition images with high recognition accuracy.

It is very challenging to detect traffic signs using a high-precision real-time approach in realistic scenes with respect to driver-assistance systems for driving vehicles and autonomous driving. To address this challenge, in this paper, a new detection scheme (named MSA_YOLOv3) is proposed to accurately achieve real-time localization and classification of small traffic signs. First, data augmentation is achieved using image mixup technology. Second, a multi-scale spatial pyramid pooling block is introduced into the Darknet53 network to enable the network to learn object features more comprehensively. Finally, a bottom-up augmented path is designed to enhance the feature pyramid in YOLOv3, and the result is to achieve accurate localization of objects by utilizing fine-grained features effectively in the lower layers. According to the tests on the TT100K dataset (which is a dataset for traffic sign detection), the performance of the proposed MSA_YOLOv3 is better than that of YOLOv3 in detecting small traffic signs. The detection speed of MSA_YOLOv3 is 23.81 FPS, and the mAP (mean Average Precision) reaches up to 86%.

It holds great implications for large-scale traffic scene understanding, to enable traffic sign detection in real environment. The large intra-category variance in features caused by instances of different spatial scales and appearances are the main problem. In this paper, a traffic sign detection framework using scale-aware and domain adaptive network (SADANet) was proposed, which seamlessly combines a multiscale prediction network (MSPN) with a domain adaptive network (DAN) in a tightly-coupled manner in order to tackle the challenge. Specifically, MSPN is dedicated to extracting the multiscale feature. It utilizes fully, the low-level location and high-level semantic information, and considers the combination of the context information and the instance specific content awareness in the scale transformation. DAN is dedicated to making features domain invariant without enough labeled test data. It aligns the domain distributions from the different scales effectively by leveraging the mapping relationship between the image representation and the multiscale features. Experimental results show that the SADANet is effective in traffic sign detection task and is also competitive when compared with the state-of-the-art methods.

With the development of science and technology, artificial intelligence has been widely used in the transportation field, and research on the symmetry of artificial intelligence has become increasingly more in-depth. Traffic sign detection based on deep learning has the problems of different target shapes and high variability in the number of targets between different labels. To solve these problems from a lack of symmetry, the idea of applying the concept of balanced data and the deformable positioning region to a target recognition network is proposed. The research is based on the improvement of the Libra R-CNN. Aiming at the problem that the difficult-to-distinguish target in target detection has a high impact on detection, the idea of generating increasingly more diverse indistinguishable samples during training is proposed to improve the detection accuracy, which is verified by experiments. The experiment is carried out on the MS COCO 2017 and traffic sign datasets. The improved Libra R-CNN is 3 percentage points better than the unimproved Libra R-CNN's mean Average Precision (mAP). A large number of comparative experimental results show that the improved network is effective.

In recent years, the deep learning is applied to the field of traffic sign detection methods which achieves excellent performance. However, there are two main challenges in traffic sign detection to be solve urgently. For one thing, some traffic signs of small size are more difficult to detect than those of large size so that the small traffic signs are undetected. For another, some false signs are always detected because of interferences caused by the illumination variation, bad weather and some signs similar to the true traffic signs. Therefore, to solve the undetection and false detection, we first propose a cascaded R-CNN to obtain the multiscale features in pyramids. Each layer of the cascaded network except the first layer fuses the output bounding box of the previous one layer for joint training. This method contributes to the traffic sign detection. Then, we propose a multiscale attention method to obtain the weighted multiscale features by dot-product and softmax, which is summed to fine the features to highlight the traffic sign features and improve the accuracy of the traffic sign detection. Finally, we increase the number of difficult negative samples for dataset balance and data augmentation in the training to relieve the interference by complex environment and similar false traffic signs. The data augment method expands the German traffic sign training dataset by simulation of complex environment changes. We conduct numerous experiments to verify the effectiveness of our proposed algorithm. The accuracy and recall rate of our method are 98.7% and 90.5% in GTSDB, 99.7% and 83.62% in CCTSDB and 98.9% and 85.6% in Lisa dataset respectively.

Traffic sign detection is one of the critical technologies in the field of intelligent transportation systems (ITS). The difficulty of traffic sign detection mainly lies in detecting small objects in a wide and complex traffic scene quickly and accurately. In this paper, we regard traffic sign detection as a region classification problem and propose a two-stage CNN-based approach to solve it. At the first stage, we design an efficient network which is built with improved fire-modules to generate object proposals quickly. The network up-samples and merges the feature maps of different scales to attain a high-resolution fused feature map which contains semantically strong features of multi-scale objects. Specially, the prediction is made on the fuse feature map and based on the novel center-point estimation. With the overall designs, our region proposal network can achieve high recall value while using low-resolution images. At the second stage, a separate classification network is proposed. The bottleneck of the classification performance is generally caused by the greatly similar appearances between traffic signs. Therefore, we further explore local regions with critical differences between traffic signs to obtain fine-grained local features which help to improve classification. Finally, we evaluate our method on a challenge benchmark Tsinghua-Tencent 100K which provides many large images with small traffic sign instances. The experiment result shows that our method has better performance and faster detection speed than many state-of-the-art traffic sign detection methods.

The three-dimensional (3-D) geographic locations of street furniture, such as traffic signs, comprise the basic content of 3-D city construction, and such information is indispensable for periodic statistics for road management and maintenance. This article presents a novel solution for acquiring 3-D information on small traffic signs based on mobile mapping system (MMS) data. First, a lightweight backbone network called VGG-L under an optimized faster region-based convolutional neural network detection framework is proposed for the detection of small traffic signs. An urban traffic sign detection (UTSD) dataset is created based on panoramic images obtained from test areas. Detection results from the UTSD dataset show that VGG-L outperforms other popular networks and achieves a mean average precision of 75.4%, which is 4.2%-14.8% higher than those of VGG16, MobileNet, ResNet, and YOLOv3. Second, a novel depth-map-based 3-D spatial geolocation method is proposed for obtaining the 3-D geographic locations of the objects. Then, a center-based method is proposed to automatically extract the final 3-D vector of the target. Experimental results illustrate that the proposed method performs 3-D positioning and vectorization of the milestones and circular and triangular traffic signs, accurately and effectively, achieving greater than 86% recall and precision for the three types of targets in the test areas. The experiments demonstrate that the overall 3-D information acquisition scheme is feasible and has great application potential.

There are known limitations in mobile omnidirectional camera systems with an equirectangular projection in the wild, such as momentum-caused object distortion within images, partial occlusion and the effects of environmental settings. The localization, instance segmentation and classification of traffic signs from image data is of significant importance to applications such as Traffic Sign Detection and Recognition (TSDR) and Advanced Driver Assistance Systems (ADAS). Works show the efficacy of using state-of-the-art deep pixel-wise methods for this task yet rely on the input of classical landscape image data, automatic camera focus and collection in ideal weather settings, which does not accurately represent the application of technologies in the wild. We present a new processing pipeline for extracting objects within omnidirectional images in the wild, with included demonstration in a Traffic Sign Detection and Recognition (TDSR) system. We compare Mask RCNN, Cascade RCNN, and Hybrid Task Cascade (HTC) methods, while testing RsNeXt 101, Swin-S and HRNetV2p backbones, with transfer learning for localization and instance segmentation. The results from our multinomial classification experiment show that using our proposed pipeline, given that a traffic sign is detected, there is above a 95% chance that it is classified correctly between 12 classes despite the limitations mentioned. Our results on the projected images should provide a path to use omnidirectional images with image processing to enable the full surrounding awareness from one image source.

Dissertação de mestrado integrado em Engenharia Eletrónica Industrial e Computadores, Os Advanced Driver Assistance Systems(ADAS) estão relacionados a vários sistemas nos veículos que se destinam a melhorar a segurança do tráfego rodoviário, ajudando os condutores a terem melhor consciência da estrada e dos seus perigos inerentes, bem como de outros motoristas nas proximidades. A deteção e o reconhecimento de sinais de trânsito são parte integrante do ADAS. Os sinais de trânsito fornecem informações sobre as regras de trânsito, condições das estradas, direções de rotas e auxiliam os motoristas para uma condução segura. Isto garante que o limite de velocidade atual e outros sinais de trânsito sejam exibidos para o motorista continuamente. O projeto de reconhecimento de sinais de trânsito tem sido um problema desafiador por muitos anos e, portanto, tornou-se um tópico de pesquisa importante e ativo na área de sistemas de transporte inteligentes. Esta tecnologia está a ser desenvolvida por uma variedade de fornecedores automóveis. Esta pode usar técnicas de processamento de imagem para detetar sinais de trânsito. Uma abordagem do problema de deteção e reconhecimento de sinais/semáforos usando Su pervised Learning é apresentada nesta dissertação para dois cenários diferentes. Nesta disser tação, para cada objetivo, são apresentadas duas abordagens diferentes de Supervised Learning, bem como um estudo estendido dos hiperparâmetros. Para os dois primeiros objetivos, foram de senvolvidas as abordagens para um robô produzido pela equipa de Condução Autónoma do Labo ratório de Automação e Robótica. O robô deve detetar e classificar corretamente o sinal/semáforo de trânsito apresentado. O terceiro objetivo é para a via pública e a abordagem desenvolvida deve detetar e classificar corretamente o sinal/semáforo de trânsito apresentado no conjunto de dados restritos., Advanced Driver Assistance Systems (ADAS) relate to various in-vehicle systems that are intended to improve road traffic safety by supporting and improve drivers awareness of the road and its dangers as well as other drivers in the vicinity. Traffic sign detection and recognition is part of ADAS. Traffic signs give knowledge about the traffic rules, road conditions, route directions and assist drivers for safe driving. This ensures that the current speed limit and other road signs are displayed to the driver on an ongoing basis. The design of traffic sign recognition has been a challenging problem for many years and therefore became an important and active research topic in the area of intelligent transport systems. This technology is being developed by a variety of automotive suppliers. Typically it uses classical image processing techniques to detect traffic signs. An approach to the problem of Traffic Sign/Light detection and recognition using Supervised Learning is presented in this dissertation for two different scenarios. Two different Supervised Learning approaches are presented for each objective as well as an extended hyperparameter study. For the first two objectives, the approaches were developed for a robot produced by the Autonomous Driving team from the Laboratório de Automação e Robótica fom University of Minho. The robot must correctly detect and classify the presented Traffic Sign/Light. The third objective is for the public road and the developed approach must correctly detect and classify the presented Traffic Sign/Light in the restrained dataset.

Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time.

The automatic recognition technology of traffic signs is crucial in the process of autonomous driving. This paper aims to solve the low recognition rate of traffic signs due to complex background interference. This study realizes the improvement of detection accuracy of the trained YOLOv3 model by adjusting the relevant parameters in the YOLOv3 model, retraining the data set, and finding the appropriate threshold value, and finally realizing the accurate recognition of traffic signs.