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With the continuous development of autonomous driving, traffic sign detection, as an essential subtask, has witnessed constant updates in corresponding technologies. Currently, traffic sign detection primarily confronts challenges such as the small size of detection targets and the complexity of detection scenarios. This paper focuses on detecting small traffic signs in low-light scenarios. To address these issues, this paper proposes a traffic sign detection method that integrates low-light image enhancement with small target detection, namely, LLE-STD. This method comprises two stages: low-light image enhancement and small target detection. Based on classic baseline models, we tailor the model structures by considering the requirements of lightweight traffic sign detection models and their adaptability to varying image qualities. The two stages are then coupled to form an end-to-end processing procedure. During experiments, we validate the performance of low-light image enhancement small target detection, and adaptability to images of different qualities using the public datasets GTSDB, TT-100K, and GLARE. Compared to classic models, LLE-STD demonstrates significant advantages. For example, the mAP results tested on the GLARE dataset show that LLE-STD outperforms RetinaNet by approximately 15%. This research can facilitate the practical application of deep learning-based intelligent methods in the field of autonomous driving. [ABSTRACT FROM AUTHOR]

Traffic sign detection is an important research content of Autonomous Driving Systems, which can effectively guide vehicles or driver to make correct decisions and reduce traffic accidents. The existing real-time traffic sign detectors have low detection accuracy for small objects. Therefore, we propose a novel real-time traffic sign detector based on YOLOv5 for the accurate detection of small objects. Specifically, we propose a new Multi-layer Interactive Residual Feature Fusion Network (MIRFFN) in the neck, which can effectively combine the position information of the low-layer feature maps with the semantic information of the high-layer feature maps, and refine the features by fusing different layers of feature maps. Then, we design a Residual Information Fusion (RIF) module for MIRFFN to fuse feature maps from different layers. The RIF module is composed of three residual blocks to refine spatial position information. Inspired by Bi-level Routing Attention effectively extracting small objects, we design a Stage Routing Attention (SRA) module in the backbone. The SRA modules can search the most relevant regions and enhance attention to small traffic signs in high-layer feature maps. We conduct experiments on GTSDB, TT100K, and CCTSDB2021, and achieve mAP of 96.2%, 72.7%, and 90.8%, respectively. Our method achieves 48.91 FPS on the GTSDB dataset. The experimental results show that our method can accurately perform real-time traffic sign detection. [ABSTRACT FROM AUTHOR]

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems rely heavily on accurate traffic sign detection (TSD) to enhance road safety and traffic management. Various methods have been explored in the literature for this purpose, with deep learning methods consistently demonstrating superior accuracy. However, existing research highlights the persistent challenge of achieving high accuracy rates while maintaining non-destructive and real-time requirements. In this study, we propose a deep learning model based on the YOLOv8 architecture to address this challenge. The model is trained and evaluated using a custom dataset, and extensive experiments and performance analysis demonstrate its ability to achieve precise results, thus offering a promising solution to the current research challenge in deep learning-based TSD. [ABSTRACT FROM AUTHOR]

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. The paper introduces an innovative solution in the form of a convolutional neural network based on the YOLOv8 architecture, underpinned by a custom dataset representing real-world traffic sign images and rigorous training. Through extensive experimentation, the proposed model not only proves highly effective but consistently achieves remarkable accuracy rates, successfully meeting the stringent real-time processing requirements crucial for self-driving systems, thus advancing the safety and efficiency of autonomous vehicles and shaping the future of transportation. [ABSTRACT FROM AUTHOR]

With the rapid promotion of autonomous driving technology, it is extremely important to scientifically anticipate the related technologies and analyze their possible impact on urban road systems. The accuracy of detection and localization of traffic elements of autonomous driving is closely related to the ability of autonomous driving devices to make control decisions and the safety of autonomous driving. The study designs a new high-speed road driving scheme based on autonomous driving by analyzing the challenges related to urban traffic that may be brought about by unmanned driving. On the basis of the faster R-CNN algorithm, the context information around the target is introduced to locate and detect small-scale traffic signs. A new pedestrian detection model is designed, which is based on the feature pyramid network and introduces the SE module to highlight the features of the visible part of the pedestrian and reduce the missed detection rate caused by inter-class occlusion. The improved traffic sign detection framework improves the detection accuracy by 18.91% compared to the original faster R-CNN, while the enhanced pedestrian inspection method improves the detection accuracy by 14.00%. For both traffic sign detection and pedestrian detection accuracy and speed are improved compared to the original method. [ABSTRACT FROM AUTHOR]

Traffic mark detection and identification play a key character in the development of driverless and intelligent transportation systems, offering significant assistance in ensuring the safety of people's daily travels. However, the detection effect of traffic signs is affected by many target categories, small targets, and low recognition accuracy, making traffic sign detection more challenging than target detection in general scenarios. In this paper, an improved YOLOv7 network (YOLOv7-COORD) is entered. Foremost, increase CBAM attention module at the connection between backbone and neck network of YOLOv7 to enhance the expression ability of neural networks through the attention mechanism, emphasizing important features and ignoring minor features to enhance the efficiency and precision of the network. Secondly, By adding CoordConv before the upsampling of the neck and before the detection head output, the network can better feel the location message in the characteristic map. Finally, a detection head generated by the low-level, high-resolution characteristic map is added to enhance the recognition accuracy of small target object. The abundance of experimental data demonstrates that the impression of the improved YOLOv7-COORD model is superior to that of the original YOLOv7 model, and the average accuracy of (mAP@0.5) on TT100K datasets is 3.2% higher than that of YOLOv7, reaching 85.4%. In summary, the improved YOLOv7-COORD model can better detect targets in traffic sign images. [ABSTRACT FROM AUTHOR]

Traffic sign detection is a crucial element of advanced driver assistance systems (ADAS) for environmental perception. However, challenges persist in the detection of small-scale targets and susceptibility to adverse weather, varying light conditions, and occlusions. To address this issue, a novel traffic sign detection algorithm, GRFS-YOLOv8, is proposed. GRFS-YOLOv8 introduces an enhanced greater receptive field-SPPF (GRF-SPPF) module to replace the original SPPF module, enabling the capture of richer multiscale features from image feature maps. Additionally, a new SPAnet architecture is designed by introducing two "shortcut" paths and additional smaller target detection layers to enhance path aggregation capabilities. This architecture propagates complete semantic information, alleviating the reduction in resolution for small targets and enhancing the model's capability to detect them. Finally, by employing GhostConv and C2fGhost to replace multiple CBS and C2f modules in Backbone and Neck, cost-effective linear operations are utilized to obtain more feature maps, thus reducing the computational cost of the model. Experimental validation across multiple datasets demonstrates the efficacy and adaptability of GRFS-YOLOv8, achieving an 80.3% mAP and 72.4% Recall in CCTSDB 2021, a 71.2% mAP and 95% Recall in TT100K, and a 94.0% mAP and 96.0% Recall in GTSDB, surpassing mainstream detectors and comparative methods. [ABSTRACT FROM AUTHOR]

A road intersection is an area where more than two roads in different directions connect. It is a point of transition where the driver navigates and makes the decision, making it an area with a high risk for traffic accidents. Road intersection detection is identifying and analyzing road intersections in real time using various technologies and algorithms. It is an essential part of intelligent transportation systems and autonomous driving. Road intersection detection helps the driver to identify the road intersection early to make good driving decisions and avoid accidents. Despite its high importance, only a few research is found regarding this topic. Existing research mainly focuses on detecting and classifying traffic signs, vehicles, and pedestrians. In this research, we propose an algorithm to detect road intersections using an image from the front-facing camera installed on the car as an input. We use traffic sign detection to detect seven types of traffic signs having a high probability of intersection nearby and combine it with our novel road intersection detection algorithm to detect the location of the road intersection. Our road inter-section detection algorithm leverages the relationship between the area of the traffic signs and the location of the intersection. Our proposed method gives promising results from the experiments and can detect road intersections from further distances. Our method is also able to perform detection in real time. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic sign detection is a crucial aspect of advanced driver assistance systems (ADAS) for academic research and the automotive industry. seeing that accurate and timely detection of traffic signs (TS) is essential for ensuring the safety of driving. However, TS detection methods encounter challenges like slow detection speed and a lack of robustness in complex environments. This paper suggests addressing these limitations by proposing the use of the you only look one version 7 (YOLOv7) network to detect and recognize TS in road scenes. Furthermore, the k-means++ algorithm is used to acquire anchor boxes. Additionally, a tiny version of YOLOv7 is used to take advantage of its real-time and low model size, which are required for real-time hardware implementation. So, we conducted an experiment using our proprietary Morocco dataset. According to the experimental results, YOLOv7 achieves 85% in terms of mean average precision (mAP) at 0.5 for all classes. And YOLOv7-tiny obtains 90% in the same term. Afterward, a recognition system for the prohibitive class using the convolutional neural network (CNN) is trained and integrated inside the YOLOv7 algorithm; its model achieves an accuracy of 99%, which leads to a good specification of the prohibitive sign meaning. [ABSTRACT FROM AUTHOR]

Traffic signs have great importance in driving safety. For the recently emerging autonomous vehicles, that can automatically detect and recognize all road inventories such as traffic signs. Firstly, in this study, a method based on a mobile mapping system (MMS) is proposed for the detection of traffic signs to establish a Turkish traffic sign dataset. Obtaining images from real traffic scenes using the MMS method enhances the reliability of the model. It is an easy method to be applied to real life in terms of both cost and suitability for mobile and autonomous systems. In this frame, YOLOv4-Tiny, one of the object detection algorithms, that is considered to be more suitable for mobile vehicles, is used to detect and recognize traffic signs. This algorithm is low operation cost and more suitable for embedded devices due to its simple neural network structure compared to other algorithms. It is also a better option for real-time detection than other approaches. For the training of the model in the suggested method, a dataset consisting partly of images taken with MMS based on realistic field measurement and partly of images obtained from open data sets was used. This training resulted in the mean average precision (mAP) value being obtained as 98.1%. The trained model was first tested on existing images and then tested in real time in a laboratory environment using a simple fixed web camera. The test results show that the suggested method can improve driving safety by detecting traffic signs quickly and accurately, especially for autonomous vehicles. Therefore, the proposed method is considered suitable for use in autonomous vehicles. [ABSTRACT FROM AUTHOR]

With the rapid development of intelligent transportation systems, lane detection and traffic sign recognition have become critical technologies for achieving full autonomous driving. These technologies offer crucial real-time insights into road conditions, with their precision and resilience being paramount to the safety and dependability of autonomous vehicles. This paper introduces an innovative method for detecting and recognizing multi-lane lines and intersection stop lines using computer vision technology, which is integrated with traffic signs. In the image preprocessing phase, the Sobel edge detection algorithm and weighted filtering are employed to eliminate noise and interference information in the image. For multi-lane lines and intersection stop lines, detection and recognition are implemented using a multi-directional and unilateral sliding window search, as well as polynomial fitting methods, from a bird's-eye view. This approach enables the determination of both the lateral and longitudinal positioning on the current road, as well as the sequencing of the lane number for each lane. This paper utilizes convolutional neural networks to recognize multi-lane traffic signs. The required dataset of multi-lane traffic signs is created following specific experimental parameters, and the YOLO single-stage target detection algorithm is used for training the weights. In consideration of the impact of inadequate lighting conditions, the V channel within the HSV color space is employed to assess the intensity of light, and the SSR algorithm is utilized to process images that fail to meet the threshold criteria. In the detection and recognition stage, each lane sign on the traffic signal is identified and then matched with the corresponding lane on the ground. Finally, a visual module joint experiment is conducted to verify the effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

With the rise of deep learning technology, significant progress has been made in object detection. Traffic sign detection is a research hotspot for object detection tasks. However, due to small size of traffic signs, there is room for further improvement in the comprehensive performance of the existing technology. In this paper, we propose a lightweight network based on yolov5s to achieve real-time localization and classification of small traffic signs. First, we improve the bottleneck transformers with 3 convolution (Bot3) module to enhance the backbone network's ability to extract features from small targets, improving the accuracy while reducing the number of parameters and giga floating-point operations per second (GFLOPs). Second, we introduce ghost convolution (GhostConv) to obtain redundant feature maps with cheap operations to further improve the model's efficiency. Finally, we use soft non-maximum suppression (Soft-NMS) in the detection phase to improve the model accuracy again without additional computational overhead for training. According to the tests on the Tsinghua-Tencent 100 K (TT100K) dataset, the proposed method outperforms the original YOLOv5s in small traffic sign detection, with an increase of 8.7% in m A P 50 , a reduction of 22.5% in parameter count, and a 17.2% reduction in computational complexity. [ABSTRACT FROM AUTHOR]

Autonomous driving technology has entered a period of rapid development, and traffic sign detection is one of the important tasks. Existing target detection networks are difficult to adapt to scenarios where target sizes are seriously imbalanced, and traffic sign targets are small and have unclear features, which makes detection more difficult. Therefore, we propose a Hybrid Feature Fusion Traffic Sign detection algorithm based on YOLOv7 (HFFT-YOLO). First, a self-attention mechanism is incorporated at the end of the backbone network to calculate feature interactions within scales; Secondly, the cross-scale fusion part of the neck introduces a bottom-up multi-path fusion method. Design reuse paths at the end of the neck, paying particular attention to cross-scale fusion of high-level features. In addition, we found the appropriate channel width through a lot of experiments and reduced the superfluous parameters. In terms of training, a new regression loss CMPDIoU is proposed, which not only considers the problem of loss degradation when the aspect ratio is the same but the width and height are different, but also enables the penalty term to dynamically change at different scales. Finally, our proposed improved method shows excellent results on the TT100K dataset. Compared with the baseline model, without increasing the number of parameters and computational complexity, and increased by 2.2% and 2.7%, respectively, reaching 92.9% and 58.1%. [ABSTRACT FROM AUTHOR]

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a mAP@0.5 of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Graphics is the property of Journal of Graphics Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traffic sign recognition plays a crucial role in enhancing the safety and efficiency of traffic systems. However, in snowy conditions, traffic signs are often obscured by particles, leading to a severe decrease in detection accuracy. To address this challenge, we propose an improved YOLOv8-based model for traffic sign recognition. Initially, we introduce a Multi-Scale Group Convolution (MSGC) module to replace the C2f module in the YOLOv8 backbone. Data indicate that MSGC enhances detection accuracy while maintaining model lightweightness. Subsequently, to improve the recognition ability for small targets, we introduce an enhanced small target detection layer, which enhances small target detection accuracy while reducing parameters. In addition, we replaced the original BCE loss with the improved EfficientSlide loss to improve the sample imbalance problem. Finally, we integrate Deformable Attention into the model to improve the detection efficiency and performance of complex targets. The resulting fused model, named MSGC-YOLOv8, is evaluated on an enhanced dataset of snow-covered traffic signs. Experimental results show that the MSGC-YOLOv8 model is used for snow road traffic sign recognition. Compared with the YOLOv8n model mAP@0.5:0.95, mAP@0.5:0.95 is increased by 17.7% and 18.1%, respectively, greatly improving the detection accuracy. Compared with the YOLOv8s model, while the parameters are reduced by 59.6%, mAP@0.5 only loses 1.5%. Considering all aspects of the data, our proposed model shows high detection efficiency and accuracy under snowy conditions. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Propose an optimized and improved traffic sign detection model based on YOLOv8n, addressing the issues of low accuracy and inaccurate detection, especially in adverse weather conditions, observed in current traditional network models. We leverage existing techniques, including the BoTNet (Bottleneck Transformers for Visual Recognition) module to enhance image classification capabilities, the ODConv (Omni-dimensional Dynamic Convolution) module to supplement attention for improved accuracy, and the LSKA (Large Separable Kernel Attention) module to reduce memory and computational complexity while enhancing small object detection capabilities. Additionally, we employ the WIoU (Wise Intersection over Union) loss function to enhance the model’s generalization performance. Without additional preprocessing to simulate adverse weather conditions, our results on the TT100K dataset, including mAP50, mAP95 (mAP is ’mean Average Precision’), and F1, relative to the original YOLOv8n model, show improvements of 3%, 4%, and 2.5% in misty conditions and 1%, 2.6%, and 1.7% in dark conditions, respectively. On the GTSDB dataset, in misty conditions, the improvements are 5%, 4.2%, and 2.3%, and in dark conditions, the improvements are 3%, 6.6%, and 6%. When 30% of the training set is augmented with fog, the detection performance of the improved model is comparable to, or even exceeds, that of the YOLOv8n model trained with the entire fog-augmented training set. This comprehensive result highlights the significant superiority of our model over the comparative model, demonstrating its practical applicability.

The challenge of evaluating deep learning-based object detection models in complex traffic scenarios, characterized by changing weather and lighting conditions, is addressed in this study. Real-world testing proves time and cost-intensive, leading to the proposal of a Video Frame Feeding (VFF) approach as a solution. The proposed Video Frame Feeding approach acts as a bridge between object detection models and simulated environments, enabling the generation of realistic scenarios. Leveraging the CarMaker (CM) tool to simulate realistic scenarios, the framework utilizes a virtual camera to capture the simulated environment and feed video frames to an object identification model. The VFF algorithm, with automated validation using simulated ground truth data, enhances detection accuracy to over 95% at 30 frames per second within 130 meters. Employing the You Only Look Once (YOLO) version 4 and the German Traffic Sign Recognition Benchmark dataset, the study assesses a traffic signboard identification model across various climatic conditions. Notably, the VFF algorithm improves accuracy by 2% to 5% in challenging scenarios like foggy days and nights. This innovative approach not only identifies object detection issues efficiently but also offers a versatile solution applicable to any object detection model, promising improved dataset quality and robustness for enhanced model performance.

Accurately detecting small traffic signs in complex and changing environments is challenging. Although the detection accuracy of traffic signs has been improved with the development of deep learning, it is not effective in detecting small traffic signs. We propose soft-channel spatial attention to solve this problem. Specifically, redundant information is suppressed while maintaining the high and low dimensional feature information, and the traffic sign region is enhanced. In addition, a pyramid scale-aware module is designed to dynamically generate different size receptive fields according to the regional context of the input image and construct a feature-rich feature pyramid to detect the diversity of object scales. Our approach is tested on public transportation sign detection datasets. A large number of experimental results have proved the superiority and effectiveness of the proposed approach compared to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traffic sign detection technology plays an important role in driver assistance systems and automated driving systems. This paper proposes DeployEase-YOLO, a real-time high-precision detection scheme based on an adaptive scaling channel pruning strategy, to facilitate the deployment of detectors on edge devices. More specifically, based on the characteristics of small traffic signs and complex background, this paper first of all adds a small target detection layer to the basic architecture of YOLOv5 in order to improve the detection accuracy of small traffic signs.Then, when capturing specific scenes with large fields of view, higher resolution and richer pixel information are preserved instead of directly scaling the image size. Finally, the network structure is pruned and compressed using an adaptive scaling channel pruning strategy, and the pruned network is subjected to a secondary sparse pruning operation. The number of parameters and computations is greatly reduced without increasing the depth of the network structure or the influence of the input image size, thus compressing the model to the minimum within the compressible range. Experimental results show that the model trained by Experimental results show that the model trained by DeployEase-YOLO achieves higher accuracy and a smaller size on TT100k, a challenging traffic sign detection dataset. Compared to existing methods, DeployEase-YOLO achieves an average accuracy of 93.3%, representing a 1.3% improvement over the state-of-the-art YOLOv7 network, while reducing the number of parameters and computations to 41.69% and 59.98% of the original, respectively, with a compressed volume of 53.22% of the previous one. This proves that the DeployEase-YOLO has a great deal of potential for use in the area of small traffic sign detection. The algorithm outperforms existing methods in terms of accuracy and speed, and has the advantage of a compressed network structure that facilitates deployment of the model on resource-limited devices. [ABSTRACT FROM AUTHOR]

Automated vehicles are a significant advancement in transportation technique, which provides safe, sustainable, and reliable transport. Lane detection, maneuver forecasting, and traffic sign recognition are the fundamentals of automated vehicles. Hence, this research focuses on developing a dynamic real-time decision-making system to obtain an effective driving experience in autonomous vehicles with the advancement of deep learning techniques. The deep learning classifier such as deep convolutional neural network (Deep CNN), SegNet and are utilized in this research for traffic signal detection, road segmentation, and lane detection. The main highlight of the research relies on the proposed Finch Hunt optimization, which involves the hyperparameter tuning of a deep learning classifier. The proposed real-time decision-making system achieves 97.44% accuracy, 97.56% of sensitivity, and 97.83% of specificity. Further, the proposed segmentation model achieves the highest clustering accuracy with 90.37% and the proposed lane detection model attains the lowest mean absolute error, mean square error, and root mean error of 17.76%, 11.32%, and 5.66% respectively. The proposed road segmentation model exceeds all the competent models in terms of clustering accuracy. Finally, the proposed model provides a better output for lane detection with minimum error, when compared with the existing model. [ABSTRACT FROM AUTHOR]

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology. [ABSTRACT FROM AUTHOR]

The challenge of evaluating deep learning-based object detection models in complex traffic scenarios, characterized by changing weather and lighting conditions, is addressed in this study. Real-world testing proves time and cost-intensive, leading to the proposal of a Video Frame Feeding (VFF) approach as a solution. The proposed Video Frame Feeding approach acts as a bridge between object detection models and simulated environments, enabling the generation of realistic scenarios. Leveraging the CarMaker (CM) tool to simulate realistic scenarios, the framework utilizes a virtual camera to capture the simulated environment and feed video frames to an object identification model. The VFF algorithm, with automated validation using simulated ground truth data, enhances detection accuracy to over 95% at 30 frames per second within 130 meters. Employing the You Only Look Once (YOLO) version 4 and the German Traffic Sign Recognition Benchmark dataset, the study assesses a traffic signboard identification model across various climatic conditions. Notably, the VFF algorithm improves accuracy by 2% to 5% in challenging scenarios like foggy days and nights. This innovative approach not only identifies object detection issues efficiently but also offers a versatile solution applicable to any object detection model, promising improved dataset quality and robustness for enhanced model performance. [ABSTRACT FROM AUTHOR]

The ability to recognize traffic signs is a critical skill for safe driving, as traffic signs provide drivers with essential information about the road conditions, potential hazards, speed limits, and other important details that can impact their driving. By recognizing and understanding traffic signs, drivers can react quickly and appropriately to different situations on the road, which helps prevent accidents and ensures the safety of all road users. As part of the Robotaxi-Full Scale Autonomous Vehicle Competition, a project was undertaken to develop a traffic sign recognition system using YOLOv8, a state-of-the-art deep learning model that can detect and classify objects in real-time. The project team trained YOLOv8 on a dataset of traffic sign images to create a model that could accurately recognize and classify different types of traffic signs. This traffic sign recognition system has the potential to significantly improve road safety by helping autonomous vehicles and human drivers to better understand their surroundings and react appropriately to changing road conditions. The system can assist drivers by providing real-time alerts and warnings about potential hazards, speed limits, and other important information. Furthermore, this project demonstrates the power and potential of deep learning and artificial intelligence in improving transportation safety and efficiency. As AI technology continues to advance, we can expect to see more innovative applications in the automotive industry that will help improve the driving experience and make our roads safer for everyone. [ABSTRACT FROM AUTHOR]

Accurate and fast detection of traffic sign information is vital for autonomous driving systems. However, the YOLOv5 algorithm faces challenges with low accuracy and slow detection when it is used for traffic sign detection. To address these shortcomings, this paper introduces an accurate and fast traffic sign detection algorithm–YOLOv5-Efficient Vision TransFormer(EfficientViT)). The algorithm focuses on improving both the accuracy and speed of the model by replacing the CSPDarknet backbone of the YOLOv5(s) model with the EfficientViT network. Additionally, the algorithm incorporates the Convolutional Block Attention Module(CBAM) attention mechanism to enhance feature layer information extraction and boost the accuracy of the detection algorithm. To mitigate the adverse effects of low-quality labels on gradient generation and enhance the competitiveness of high-quality anchor frames, a superior gradient gain allocation strategy is employed. Furthermore, the strategy introduces the Wise-IoU (WIoU), a dynamic non-monotonic focusing mechanism for bounding box loss, to further enhance the accuracy and speed of the object detection algorithm. The algorithm's effectiveness is validated through experiments conducted on the 3L-TT100K traffic sign dataset, showcasing a mean average precision (mAP) of 94.1% in traffic sign detection. This mAP surpasses the performance of the YOLOv5(s) algorithm by 4.76% and outperforms the baseline algorithm. Additionally, the algorithm achieves a detection speed of 62.50 frames per second, which is much better than the baseline algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to improve the detection speed of road traffic signs, an improved model based on lightweight YOLOv5 was proposed. Firstly, Ghost convolution and depthwise convolution were used to build a new Bottleneck, which could reduce the amount of computation and parameters. Then the BiFPN structure was introduced, which could enhance the feature fusion ability. CIoU loss function was replaced by SIoU loss function, which focused on the angle information of ground true box and prediction one, so that it would improve the detection accuracy. Secondly, the TT100K dataset was optimized, and 24 categories of traffic sign pictures and labels with more than 200 were screened out. Finally, the experiment achieved 84% accuracy, 81.2% recall and 85.4% mAP@ 0.5. Com pared with the original YOLOv5 model, the number of parameters was reduced by 29.0%, the amount of computation was reduced by 29.4%, but the mAP@ 0.5 was only reduced by 0.1 percentages, and the detection frame rate was improved by 34 frames/ s. Using the improved model for detection, the detection speed could be significantly improved, could basically achieve the goal of compression model on the basis of maintaining the detection accuracy. [ABSTRACT FROM AUTHOR]

In recent years, significant progress has been witnessed in the field of deep learning-based object detection. As a subtask in the field of object detection, traffic sign detection has great potential for development. However, the existing object detection methods for traffic sign detection in real-world scenes are plagued by issues such as the omission of small objects and low detection accuracies. To address these issues, a traffic sign detection model named YOLOv7-Traffic Sign (YOLOv7-TS) is proposed based on sub-pixel convolution and feature fusion. Firstly, the up-sampling capability of the sub-pixel convolution integrating channel dimension is harnessed and a Feature Map Extraction Module (FMEM) is devised to mitigate the channel information loss. Furthermore, a Multi-feature Interactive Fusion Network (MIFNet) is constructed to facilitate enhanced information interaction among all feature layers, improving the feature fusion effectiveness and strengthening the perception ability of small objects. Moreover, a Deep Feature Enhancement Module (DFEM) is established to accelerate the pooling process while enriching the highest-layer feature. YOLOv7-TS is evaluated on two traffic sign datasets, namely CCTSDB2021 and TT100K. Compared with YOLOv7, YOLOv7-TS, with a smaller number of parameters, achieves a significant enhancement of 3.63% and 2.68% in the mean Average Precision (mAP) for each respective dataset, proving the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes a detector that focuses on multi-scale detection problems and effectively enhances the detection performance to solve the problem that is hard to detect minor traffic signs. This detector, called YOLOF-F (you only look one-level feature fusion), is a single-stage detector that extracts multi-scale feature information from a single layer of fusion feature. First, we propose FFM (feature fusion module) to fuse different scales. Next, we offer a new encoder CDE (corner dilated encoder) to enhance the angular point information in the feature map, improve position regression accuracy, and maintain a faster detection speed. Finally, YOLOF-F achieved 74.57% and 77.23% of the AP on the GTSDB and CTSD datasets and reached 32 FPS. Extensive experiments validate that YOLOF-F is faster and more effective than most traffic sign detection methods. [ABSTRACT FROM AUTHOR]

Traffic sign detection (TSD) is crucial for real-world applications like driverless vehicles, intelligent driver-assistance systems, and traffic management. Recent advancements in TSD have demonstrated promising outcomes. Nonetheless, challenges persist in terms of speed, accuracy, memory consumption, the capability of the backbone to generate features, and computational cost, especially in handling diverse traffic sign characteristics. To overcome these challenges, we propose Sign-YOLO (You Only Look Once), a novel attention-based one-stage method that integrates YOLOv7 with the squeeze-and-excitation (SE) model and special attention mechanism. Sign-YOLO enhances the feature representation capacity of the model in the presence of variations in traffic sign sizes. The SE block adjusts channel-specific feature responses by actively considering the relationships between channels. By selectively focusing on relevant features, the attention mechanism helps the model better capture and understand the distinctive characteristics of traffic signs, thereby improving detection accuracy. Sign-YOLO effectively reduces the computational cost and memory consumption; further, it effectively enriches the robustness of extracted features. The proposed approach enables the model to allocate more attention to relevant regions of the input, thereby reducing the impact of size discrepancies and contributing to the overall robustness of the system. The experimental findings highlight the success of Sign-YOLO in TSD tasks. Our proposed method exhibits cutting-edge performance on the German Traffic Signs Detection Benchmark (GTSDB) dataset, simultaneously achieving a 98% reduction in model size and memory consumption. Sign-YOLO attains a 99.10% mean average precision (mAP) on the GTSDB dataset. In comparison to both two- and one-stage detectors, our approach exhibits an improvement of 3.33%. The proposed approach is the swiftest and most lightweight framework in terms of memory usage, making it the ideal option for implementation in real-time applications.

As intelligent transportation systems continue to evolve, effective traffic sign detection is crucial for enhancing road safety and managing traffic congestion efficiently. This paper introduces the YOLOv7-tiny-RCA model, a novel lightweight approach designed specifically to address the dual challenges of high model complexity and diminished detection accuracy in long-distance scenarios commonly faced in traffic sign recognition. By integrating an improved ELAN-REP module into the YOLOv7-tiny backbone, our model significantly reduces computational complexity during the inference stage. Additionally, the incorporation of the CBAM attention mechanism enhances the model’s capability to extract and fuse relevant information from traffic scenes more effectively. To further optimize performance, we replaced the traditional feature fusion network with an Asymptotic Feature Pyramid Network, which facilitates better interaction between different layers and reduces the overall computational burden. Our experimental results demonstrate that the YOLOv7-tiny-RCA model achieves a mean Average Precision of 81.03% and a parameter reduction from 6.13M to 4.99M, highlighting its efficiency and potential for deployment on edge devices. These significant improvements indicate that our model not only advances traffic sign detection technologies but also offers practical applications for modern intelligent transportation systems.

Purposes Aiming at the problems of large computation and high reference quantity in existing traffic sign detection algorithms, in this paper we proposed a lightweight traffic sign detection algorithm based on improved YOLOv7. Methods The algorithm is divided into four parts: input, backbone network of feature extraction, neck network of feature fusion, and head network of target prediction. Large kernel convolution was introduced into the backbone network, which increases the effective receptive field and improves the ability of feature extraction. The detection of neck fusion coordinate attention, random pooling, and other methods can not only build channel attention and capture accurate position, but also improve the generalization ability of the network. In addition, a comprehensive depth-separable convolution module was proposed to extract image features by reducing the number of parameters and the radical sign. Results Experimental results show that the detection accuracy of the proposed algorithm on the CCTSDB2021 data set reaches 93.13%, and mAP also reaches 87.59%, which is a great improvement with respect to other methods of the same type. The network achieves a high accuracy rate under the condition of low parameter number and calculation amount, which can not only accurately capture the location information of traffic signs, but also achieve a high accuracy rate. At the same time, it can accurately predict the traffic signs.

Traffic sign detection has received widespread concern in recent years. However, existing methods often fail to meet the real-time detection requirements, and there are many cases of missing detection in small-scale traffic sign detection. To solve these problems, a real-time traffic sign detection algorithm combining attention mechanism and contextual information is proposed. Using YOLOv5 as the base model, firstly, spatial attention mechanism is embedded in the backbone to adaptively enhance the features of important positions and suppress interference information to improve the feature extraction capability of the backbone network. Secondly, the cross stage partial window Transformer module is designed to learn correlations of different locations and to capture rich contextual information around traffic signs, which is beneficial to improving the detection accuracy of small-scale traffic signs. Thirdly, the lightweight feature fusion network is proposed to fuse the feature maps of different scales, which can reduce the computational burden and ensure the effective feature fusion. Finally, in the post-processing stage, Gaussian weighted fusion is used to amend the prediction boxes to improve the positioning accuracy. Experiments on TT100K and DFG traffic sign detection datasets show that the proposed method can effectively improve the missing detection of small-scale traffic signs, with higher accuracy and real-time performance, and can meet the requirements of traffic sign detection in actual scenarios.

With the continuous development of autonomous driving, traffic sign detection, as an essential subtask, has witnessed constant updates in corresponding technologies. Currently, traffic sign detection primarily confronts challenges such as the small size of detection targets and the complexity of detection scenarios. This paper focuses on detecting small traffic signs in low-light scenarios. To address these issues, this paper proposes a traffic sign detection method that integrates low-light image enhancement with small target detection, namely, LLE-STD. This method comprises two stages: low-light image enhancement and small target detection. Based on classic baseline models, we tailor the model structures by considering the requirements of lightweight traffic sign detection models and their adaptability to varying image qualities. The two stages are then coupled to form an end-to-end processing procedure. During experiments, we validate the performance of low-light image enhancement small target detection, and adaptability to images of different qualities using the public datasets GTSDB, TT-100K, and GLARE. Compared to classic models, LLE-STD demonstrates significant advantages. For example, the mAP results tested on the GLARE dataset show that LLE-STD outperforms RetinaNet by approximately 15%. This research can facilitate the practical application of deep learning-based intelligent methods in the field of autonomous driving.

As a fundamental element of the transportation system, traffic signs are widely used to guide traffic behaviors. In recent years, drones have emerged as an important tool for monitoring the conditions of traffic signs. However, the existing image processing technique is heavily reliant on image annotations. It is time consuming to build a high-quality dataset with diverse training images and human annotations. In this paper, we introduce the utilization of Vision–language Models (VLMs) in the traffic sign detection task. Without the need for discrete image labels, the rapid deployment is fulfilled by the multi-modal learning and large-scale pretrained networks. First, we compile a keyword dictionary to explain traffic signs. The Chinese national standard is used to suggest the shape and color information. Our program conducts Bootstrapping Language-image Pretraining v2 (BLIPv2) to translate representative images into text descriptions. Second, a Contrastive Language-image Pretraining (CLIP) framework is applied to characterize not only drone images but also text descriptions. Our method utilizes the pretrained encoder network to create visual features and word embeddings. Third, the category of each traffic sign is predicted according to the similarity between drone images and keywords. Cosine distance and softmax function are performed to calculate the class probability distribution. To evaluate the performance, we apply the proposed method in a practical application. The drone images captured from Guyuan, China, are employed to record the conditions of traffic signs. Further experiments include two widely used public datasets. The calculation results indicate that our vision–language model-based method has an acceptable prediction accuracy and low training cost.

The Advanced Driver Assistance System (ADAS) has emerged as a critical technology for enhancing road safety and preventing accidents. This research paper aims to provide a comprehensive study on the development and implementation of an ADAS through the utilization of a traffic sign detection Android app. One of the key functionalities of the ADAS is real-time updates on the current speed of the vehicle. By constantly monitoring the speed, the system can provide timely warnings to the driver in case they exceed the speed limit, thereby reducing the likelihood of ac-accidents caused by speeding. ADAS integrates driver drowsiness detection using smart spectacles equipped with eye activity sensors. These spectacles can monitor the driver's eye movements and identify signs of drowsiness or distraction. In such cases, the system can issue alerts or recommendations to the driver, encouraging them to take a break or refocus their attention on the road. By integrating these functionalities, the ADAS aims to enhance road safety, assist drivers with timely information and warnings, and ultimately reduce the risk of accidents on the roads. [ABSTRACT FROM AUTHOR]

Nowadays autonomous vehicles are evolving due to the advancements in cutting edge technologies. In order to recognize the traffic signatures with high efficacy, traffic recognition system is required. Sign detection and classification are the two parts of the recognition system. The sign detection algorithm detects the size and coordinates of the sign board in an image and in sign classification, the representation of traffic signal is identified and classified into one of their traffic sign sub-classes. In order to achieve these goals, an extremely fast detection module using support vector machine is proposed to detect the traffic sign into one of the traffic classes such as, prohibitory, danger, mandatory, and non-sign. Further classification is carried out using deep convolutional neural networks to determine the sub-classes of each super-class, such as, prohibitory, danger, and mandatory. Based on publicly available benchmark traffic sign image datasets, we have demonstrated that the proposed approach has significantly improved traffic sign recognition accuracy compared with state-of-the-art systems. [ABSTRACT FROM AUTHOR]

Fast and accurate detection of traffic signs is crucial for the development of intelligent transportation systems. To address the issue of false detection and missing detection of small traffic signs in complex scenes, this paper proposes a YOLO-SG model based on YOLOv5. The YOLO-SG approach employs SPD-Conv as a down-sampling structure to mitigate the loss of feature information during the down-sampling process. This enhances the detection performance of small objects in complex scenes and improves the generalization and robustness of the model. The feature extraction architecture uses GhostNet, which effectively reduces the number of model parameters and weight, enhancing the feasibility of practical model deployment. Furthermore, this study optimizes the output feature structure by introducing a small object detection layer and removing the large object detection layer, enabling the detection of small objects. Extensive experiments conducted on the GTSDB and TT100K datasets demonstrate that YOLO-SG exhibits excellent detection performance. On the GTSDB dataset, YOLO-SG achieved a 2.3% increase in mAP compared to the baseline network, while reducing the number of parameters by 42%. Similarly, on the TT100K dataset, YOLO-SG increased mAP by 6.3% and reduced the number of parameters by 43%. These experimental results showcase the effectiveness and accuracy of YOLO-SG, particularly in detecting small traffic signs. [ABSTRACT FROM AUTHOR]

An essential component of many user applications today, as well as security surveillance systems, text recognition, and some diagnosis for diseases from few scans like CT or MRI, is object identification, a computer vision problem. Also if we can see one of the most important components like object detection which can help detect any object etc. The traffic sign recognition is also a main important task which have a crucial role in intelligent autonomous vehicles. In our work we have implemented different models of YOLO v5 (You only look once) algorithm for traffic sign detection. To train yolo algorithm for the traffic sign detection on German Traffic Sign detection benchmark which have multiple scenario of images like moving vehicle images, clear images, uncleared images, and different environmental conditional images all these will help in to detect the four different classes and improve the mAP of the model with the existing work and comparing results of different models of Yolo v5 algorithm with the existing work. [ABSTRACT FROM AUTHOR]

One important task for autonomous driving is the precise detection and recognition of road traffic signs. This research focuses on a comprehensive set of 72 distinct traffic signs that are prevalent on urban roads in China, with the goal of developing an enhanced You Only Look Once (YOLO) network model tailored for this specific task. The modifications include the omission of the terminal convolution module and Conv3 (C3) module within the backbone network. Additionally, the 32-fold downsampling is replaced with a 16-fold downsampling, and a feature fusion module with dimensions of 152 × 152 is introduced in the feature layer. To capture a more encompassing context, a novel hybrid space pyramid pooling module, referred to as Hybrid Spatial Pyramid Pooling Fast (H-SPPF), is introduced. Furthermore, a channel attention mechanism is integrated into the framework, combined with three other improved methodologies. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 91.72%, a recall rate of 91.77%, and a mean average precision (mAP) of 93.88% at an intersection over union (IoU) threshold of 0.5. Additionally, the method also achieves an mAP of 75.81% for a variety of IoU criteria between 0.5 and 0.95. These achievements are validated on an augmented dataset established for this study. [ABSTRACT FROM AUTHOR]

The timely and precise discovery of traffic signs is considered an effective part of modeling automated vehicle driving. However, the dimension of traffic signs accounted for a lower ratio of input pictures which elevated the complexity of discovery. Hence, a new model is devised using faster region-based convolution neural network (faster R-CNN) traffic for detecting traffic signs. The Region of Interest (RoI) extraction and Median filter are executed for discarding the superfluous data from the dataset. The method extracted a Pyramid Histogram of Oriented Gradient (PHoG), local vector pattern (LVP), CNN and ResNet features for generating beneficial information. It is used to lessen the loss of contextual data and the data augmentation is further applied for making the training of the model more stable and time-saving. The traffic sign recognition is executed with faster R-CNN wherein the tuning of hyperparameters such as batch normalization rate, epoch and learning rate is determined by the proposed pelican cuckoo search (PCS). The method revealed improved efficacy without presenting additional computational costs in the model. Moreover, the faster R-CNN is termed the finest technique to enhance the discovery of traffic signs. The proposed PCS-based faster R-CNN outperformed with the highest precision 92.7%, specificity of 93.7% and F -measure of 93.2%. [ABSTRACT FROM AUTHOR]

An algorithm based on improved YOLOv5s is proposed to address the problems of small percentage of traffic signs in the image, low detection accuracy and complex surrounding environment. First, the attention mechanism of ECA (Efficient Channel Attention) is added to the backbone network part to enhance the feature extraction ability of the network and effectively solve the problem of complex surrounding environment. Second, the HASPP (Hybrid Atrous Spatial Pyramid Pooling) is proposed, which enhances the network's ability to combine context. Finally, the neck structure in the network is modified to allow efficient fusion of high level features with underlying features while avoiding information loss across convolutional layers. Experimental results show that the improved algorithm achieves an average detection accuracy of 94.4%, a recall rate of 74.1% and an accuracy rate of 94. 0% on the traffic signage dataset, which were 3.7,2.8, and 3. 4 percentage points higher than the original algorithm, respectively. [ABSTRACT FROM AUTHOR]