Search

In the process of metal production and manufacturing, the surface of the metal will produce defects of different scales, which will seriously affect the quality and performance of the metal, so it is very necessary to detect the defects on the metal surface. The traditional target detection method has the problem of high missing rate and low detection accuracy when detecting multiscale defects on metal surface, so it can not realize the efficient identification of different scale defects on metal surface. To solve these problems, a multiscale defect detection model S6SC-YOLOv5 based on YOLOv5 is proposed in this paper. Firstly, the neck structure was modified to an S6 feature fusion structure to improve the recognition ability of multiscale defects on metal surfaces. Secondly, the neck network is replaced by Slim-Neck to improve the fusion ability of multiscale defect features on metal surfaces. Finally, the up-sampling operator CARAFE module is used to increase the receptive field of the network. The experimental results show that S6SC-YOLOv5 is superior to YOLOv5s in overall performance. The mean average precision (mAP) of the S6SC-YOLOv5 model in the aluminum and NEU-DET data sets is 91.2% and 89.3%, respectively, which is 3.7% and 6.5% higher than that of YOLOv5s. It provides a new solution for multiscale defect detection on metal surfaces. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department 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.)

Copyright of Cotton Textile Technology is the property of Cotton Textile Technology 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.)

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature. [ABSTRACT FROM AUTHOR]

The working environment of mining unmanned vehicles features complex lighting conditions, leading to frequent occurrences of missed detections in pedestrian detection, which undermines the reliability and safety of these vehicles. To address the challenges posed by intricate tunnel lighting conditions, a low-light image enhancement algorithm was proposed. This algorithm decomposed low-light images from the RGB color space into the HSV color space, applied a Logarithm function to enhance the V component, and employed a bilateral filter to reduce noise. Morphological operations were applied to the S component for closing, followed by Gaussian filtering to further eliminate noise. The enhanced image was then transformed back into the RGB color space and subjected to a semi-implicit ROF denoising model for additional noise reduction, resulting in an enhanced image. To tackle issues of missed detections and low accuracy in pedestrian detection, an improved YOLOv3-based pedestrian detection algorithm for mining unmanned vehicles was introduced. This approach replaced the Residual connections in YOLOv3 with densely connected modules to enhance feature map utilization. Additionally, a Slim-neck structure optimized the feature fusion architecture of YOLOv3, facilitating efficient information fusion between feature maps and further improving the detection accuracy for small-target pedestrians, while its unique lightweight convolutional structure enhanced detection speed. Finally, a lightweight convolutional block attention module (CBAM) was integrated to improve attention to object categories and locations, thereby enhancing pedestrian detection accuracy. Experimental results demonstrated that the proposed low-light image enhancement algorithm effectively improved image visibility, making pedestrian textures clearer and achieving better noise suppression. The average precision of the pedestrian detection algorithm for mining unmanned vehicles based on enhanced images reached 95.68%, representing improvements of 2.53%, 6.42%, and 11.77% over YOLOv5, YOLOv3, and a coal mine key position personnel unsafe behavior recognition method based on improved YOLOv7 and ByteTrack, respectively, with a runtime of 29.31 ms. YOLOv3 and a coal mine key position personnel unsafe behavior recognition method based on improved YOLOv7 and ByteTrack experienced missed detections and false positives based on enhanced images, while the proposed pedestrian detection algorithm effectively mitigated these issues.

Insulator defect detection plays a critical role in ensuring electrical equipment's safe and stable operation, meeting the public's demand for electricity consumption. However, extracting features of insulator defects poses challenges due to complex backgrounds, variations in target sizes leading to potential oversights, and low detection accuracy. We propose an improved YOLOv8n-based insulator defect detection model to achieve timely and precise real-time detection. Firstly, the TripletAttention Module is introduced to enhance the network's ability to extract insulator defect features and reduce background interference in detection. Secondly, SCConv (Spatial and Channel Reconstruction Convolution) is utilized to redesign the detection head, proposing a more lightweight SC-Detect to replace the original one, thereby restricting feature redundancy and enhancing feature representation capability. Finally, Slim-neck based on GSConv is employed to reconstruct the neck structure, enabling the network to achieve lightweight while possessing relatively stronger feature extraction and perceptual capabilities. Experimental results demonstrate that the improved insulator defect detection network achieves an accuracy of 96.1%, a recall rate of 94.8%, a mAP@0.5 of 97.2%, and a mAP@0.5 - 0.95 of 72%, representing increases of 1.5%, 4.2%, 2.5%, and 6%, respectively. Additionally, the parameter count decreases by 22%, and computational load reduces by 39%, thereby meeting the high-precision and real-time requirements for outdoor insulator defect detection tasks. [ABSTRACT FROM AUTHOR]

As one of the most important economic crops, grapes have attracted considerable attention due to their high yield, rich nutritional value, and various health benefits. Identifying grape bunches is crucial for maintaining the quality and quantity of grapes, as well as managing pests and diseases. In recent years, the combination of automated equipment with object detection technology has been instrumental in achieving this. However, existing lightweight object detection algorithms often sacrifice detection precision for processing speed, which may pose obstacles in practical applications. Therefore, this thesis proposes a lightweight detection method named YOLOv8s-grape, which incorporates several effective improvement points, including modified efficient channel attention (MECA), slim-neck, new spatial pyramid pooling fast (NSPPF), dynamic upsampler (DySample), and intersection over union with minimum point distance (MPDIoU). In the proposed method, MECA and NSPPF enhance the feature extraction capability of the backbone, enabling it to better capture crucial information. Slim-neck reduces redundant features, lowers computational complexity, and effectively reuses shallow features to obtain more detailed information, further improving detection precision. DySample achieves excellent performance while maintaining lower computational costs, thus demonstrating high practicality and rapid detection capability. MPDIoU enhances detection precision through faster convergence and more precise regression results. Experimental results show that compared to other methods, this approach performs better in the grapevine bunch detection dataset and grapevine bunch condition detection dataset, with mean average precision (mAP50-95) increasing by 2.4% and 2.6% compared to YOLOv8s, respectively. Meanwhile, the computational complexity and parameters of the method are also reduced, with a decrease of 2.3 Giga floating-point operations per second and 1.5 million parameters. Therefore, it can be concluded that the proposed method, which integrates these improvements, achieves lightweight and highprecision detection, demonstrating its effectiveness in identifying grape bunches and assessing biophysical anomalies. [ABSTRACT FROM AUTHOR]

Cotton is one of the largest producing and consuming crops in China. Accurate detection of cotton pests is an important premise for improving the cotton quality. In this study, an ECSF-YOLOv7 pest detection model was proposed to address the high insect similarity and serious background interference in the natural environments of cotton fields. Firstly, EfficientFormerV2 was used as the feature extraction network, in order to strengthen the feature extraction of the network with the smaller number of parameters of the model. At the same time, the convolutional block attention module (CBAM) was embedded in the backbone output of the model, in order to enhance the extraction of small targets and weaken background interference; Secondly, GSConv was used to build a Slim-Neck network structure, which was reduced the number of model parameters while maintaining the recognition accuracy. Finally, Focal EIOU loss was used as the bounding box regression loss function to accelerate the network convergence for high detection accuracy. The dataset was selected as 17 types of insect images in cotton fields. The python scripts were used to enhance the annotated images, including random brightness, random flipping, mirror transformation, and Gaussian noise. The robustness of the model was also improved to build more insect recognition scenes in natural environments. Finally, a total of 6 273 images of the cotton field insect dataset were obtained with sufficient sample quantity and relatively balanced distribution. Four experiments were conducted to verify the excellent performance of the improved model, including ablation experiments, gradient-weighted class activation mapping (Grad-CAM) of attention mechanism, loss function, and mainstream model performance. Ablation experiments showed that the improved modules had a positive effect. The feature extraction of the image also varied, when CBAM was embedded in the different positions of the model. The Grad-CAM was used to generate a heat map of object detection. The region of interest of the heat map was closer to the real pest area, and less affected by background interference when the CBAM was embedded in the backbone output of the model. Five bounding box loss functions were compared: DIOU, EIOU, MPDIOU, CIOU, and Focal EIOU. Since the Focal loss function was combined to automatically adjust the loss weights of different types of samples, the Focal-EIOU bounding box loss function achieved the best overall performance and the highest detection accuracy. The results showed that the mean average precision (mAP) of the ECSF-YOLOv7 model was 95.71%, which was 1.43, 9.08, 1.94, and 1.52 percentage points higher than the mainstream object models YOLOv7, SSD, YOLOv5l, and YOLOX, respectively. The improved model was only 20.82 M in the number of model parameters, which was reduced by 44.15, 12.26, 55.25, and 17.9 percentage points, respectively. The ECSF-YOLOv7 model had an average detection speed of 69.47 frames per second, which was 5.26 frames higher than the YOLOv7 model. The high detection accuracy was also obtained in the situations of insect overlap, high similarity between species, small targets, and background interference. In summary, the ECSF-YOLOv7 model can be expected with high detection accuracy, fast detection speed, and smaller parameter quantity. The finding can provide technical support for the rapid and accurate detection of cotton field pests. [ABSTRACT FROM AUTHOR]

Introduction: Accurate detection of potato seedlings is crucial for obtaining information on potato seedlings and ultimately increasing potato yield. This study aims to enhance the detection of potato seedlings in drone-captured images through a novel lightweight model. Methods: We established a dataset of drone-captured images of potato seedlings and proposed the VBGS-YOLOv8n model, an improved version of YOLOv8n. This model employs a lighter VanillaNet as the backbone network instead of the original YOLOv8n model. To address the small target features of potato seedlings, we introduced a weighted bidirectional feature pyramid network to replace the path aggregation network, reducing information loss between network layers, facilitating rapid multi-scale feature fusion, and enhancing detection performance. Additionally, we incorporated GSConv and Slim-neck designs at the Neck section to balance accuracy while reducing model complexity. Results: The VBGS-YOLOv8n model, with 1,524,943 parameters and 4.2 billion FLOPs, achieves a precision of 97.1%, a mean average precision of 98.4%, and an inference time of 2.0ms. Comparative tests reveal that VBGS-YOLOv8n strikes a balance between detection accuracy, speed, and model efficiency compared to YOLOv8 and other mainstream networks. Specifically, compared to YOLOv8, the model parameters and FLOPs are reduced by 51.7% and 52.8% respectively, while precision and a mean average precision are improved by 1.4% and 0.8% respectively, and the inference time is reduced by 31.0%. Discussion: Comparative tests with mainstream models, including YOLOv7, YOLOv5, RetinaNet, and QueryDet, demonstrate that VBGS-YOLOv8n outperforms these models in terms of detection accuracy, speed, and efficiency. The research highlights the effectiveness of VBGS-YOLOv8n in the efficient detection of potato seedlings in drone remote sensing images, providing a valuable reference for subsequent identification and deployment on mobile devices. [ABSTRACT FROM AUTHOR]

To address the issues of complex algorithm models, poor accuracy, and low real-time performance in the coal industry's coal gangue sorting, a lightweight real-time detection method called YOLOv8s-GSC is proposed based on the characteristics of coal gangue. This method incorporates the ghost module into the YOLOv8s backbone network to reduce the network's parameter count. Additionally, a slim-neck model is used for feature fusion, and a coordinate attention module is added to the backbone network to enhance the network's feature representation capability. The experimental results show: (1) The average precision of the YOLOv8s-GSC model is 91.2%, which is a 0.6% improvement over the YOLOv8s model. The parameters and floating-point computation are reduced by 36.0% and 41.6%, respectively. (2) Compared to other models such as FasterRCNN-resnet50, SSD-VGG16, YOLOv5s, YOLOv7, YOLOv8s-Mobilenetv3, and YOLOv8s-GSConv, the average precision is improved to varying degrees. (3) The YOLOv8s-GSC model achieves a detection speed of 115FPS, meeting the real-time requirements for coal gangue detection. In conclusion, the proposed YOLOv8s-GSC model provides a lightweight, real-time, and efficient detection method for coal gangue separation in the coal industry, demonstrating high practical value. [ABSTRACT FROM AUTHOR]

The detection of underground personnel is one of the key technologies in computer vision. However, this detection technique is susceptible to complex environments, resulting in low accuracy and slow speed. To accurately detect underground coal mine operators in complex environments, we combine the underground image features with K-means++ clustering anchor frames and propose a new Re-parameterization YOLO (Rep-YOLO) detection algorithm. First, the Criss-Cross-Vertical with Channel Attention (CVCA) mechanism is introduced at the end of the network to capture the Long-Range Dependencies (LRDs) in the image. This mechanism also emphasizes the significance of different channels to enhance image processing performance and improve the representation ability of the model. Second, the new Deep Extraction of Re-parameterization (DER) backbone network is designed, which adopts the re-parameterization structure to reduce the number of parameters and computation of the model. Additionally, each DER-block fuses different scales of features to enhance the accuracy of the model’s detection capabilities. Finally, Rep-YOLO is optimized using a slim-neck structure, which reduces the complexity of the Rep-YOLO while maintaining sufficient accuracy. The results showed that the Rep-YOLO model proposed in this paper achieved an accuracy of 87.5 % , a recall rate of 77.2 % , an Average Precision (AP) of 83.1 % , and a Frame Per Second (FPS) of 71.9. Compared to eight different models, the recall, AP50, and FPS of the Rep-YOLO model were improved. The research shows that the Rep-YOLO model can provide a real-time and efficient method for mine personnel detection. Source code is released in . [ABSTRACT FROM AUTHOR]

Plastic lunch box is a topic that food safety has been neglected, and there are few studies on the defects of plastic lunch box production. A multi-scale attention mechanism based on dynamic convolution is designed in this paper to solve the problems of large differences in surface defects of plastic lunch boxes and insensitive perception of multi-scale features. This attention mechanism enables the network to capture complex features adaptively, and enhances the perception of feature channel information and spatial information at various scales. Firstly, this paper integrates the attention mechanism into Slim-neck, and enhances the model’s ability to perceive multi-scale feature information. Secondly, a small target detection layer is added to Slim-neck to solve the semantic information loss problem of various defect features. Then dynamic convolution is integrated into YOLOv8n backbone network to capture complex features adaptively. Finally, MPDIoU is used as the boundary frame loss function, and geometric characteristics are used to improve the model’s perception ability of various defects. Experimental results show that the improved model YOLOv8n-D2SM in this paper achieves 82.8% mAP@0.5 index on the plastic lunch box dataset, which is 10% higher than that of the original model. The detection speed is 26 frames/s, and the number of model parameters is basically consistent with that of the original model. This improvement makes the model more adaptable and reliable in the task of surface defect detection of plastic lunch boxes, which is convenient for deployment and application in actual production scenarios.

Wearing safety helmets can effectively reduce the risk of head injuries for construction workers in high-altitude falls. In order to address the low detection accuracy of existing safety helmet detection algorithms for small targets and complex environments in various scenes, this study proposes an improved safety helmet detection algorithm based on YOLOv8, named YOLOv8n-SLIM-CA. For data augmentation, the mosaic data augmentation method is employed, which generates many tiny targets. In the backbone network, a coordinate attention (CA) mechanism is added to enhance the focus on safety helmet regions in complex backgrounds, suppress irrelevant feature interference, and improve detection accuracy. In the neck network, a slim-neck structure fuses features of different sizes extracted by the backbone network, reducing model complexity while maintaining accuracy. In the detection layer, a small target detection layer is added to enhance the algorithm’s learning ability for crowded small targets. Experimental results indicate that, through these algorithm improvements, the detection performance of the algorithm has been enhanced not only in general scenarios of real-world applicability but also in complex backgrounds and for small targets at long distances. Compared to the YOLOv8n algorithm, YOLOv8n-SLIM-CA shows improvements of 1.462%, 2.969%, 2.151%, and 3.549% in precision, recall, mAP50, and mAP50-95 metrics, respectively. Additionally, YOLOv8n-SLIM-CA reduces the model parameters by 6.98% and the computational load by 9.76%. It is capable of real-time and accurate detection of safety helmet wear. Comparison with other mainstream object detection algorithms validates the effectiveness and superiority of this method.

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature.

Introduction: Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy. Methods: This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neckmodule with a lighter Slim-neckmodule, and improves the loss function for GIoU. Results: The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5-0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%. Discussion: With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection. [ABSTRACT FROM AUTHOR]

Winter jujube is a popular fresh fruit in China for its high vitamin C nutritional value and delicious taste. In terms of winter jujube object detection, in machine learning research, small size jujube fruits could not be detected with a high accuracy. Moreover, in deep learning research, due to the large model size of the network and slow detection speed, deployment in embedded devices is limited. In this study, an improved Yolov5s (You Only Look Once version 5 small model) algorithm was proposed in order to achieve quick and precise detection. In the improved Yolov5s algorithm, we decreased the model size and network parameters by reducing the backbone network size of Yolov5s to improve the detection speed. Yolov5s's neck was replaced with slim-neck, which uses Ghost-Shuffle Convolution (GSConv) and one-time aggregation cross stage partial network module (VoV-GSCSP) to lessen computational and network complexity while maintaining adequate accuracy. Finally, knowledge distillation was used to optimize the improved Yolov5s model to increase generalization and boost overall performance. Experimental results showed that the accuracy of the optimized Yolov5s model outperformed Yolov5s in terms of occlusion and small target fruit discrimination, as well as overall performance. Compared to Yolov5s, the Precision, Recall, mAP (mean average Precision), and F1 values of the optimized Yolov5s model were increased by 4.70%, 1.30%, 1.90%, and 2.90%, respectively. The Model size and Parameters were both reduced significantly by 86.09% and 88.77%, respectively. The experiment results prove that the model that was optimized from Yolov5s can provide a real time and high accuracy small winter jujube fruit detection method for robot harvesting. [ABSTRACT FROM AUTHOR]

Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation. [ABSTRACT FROM AUTHOR]

To enhance the speed and accuracy of insulator detection in unmanned aerial vehicle aerial photography, this study introduces a lightweight network, L-YOLOv8s, based on you only look once v8 (YOLOv8), designed for real-time detection of insulators and their defects. Initially, a lightweight backbone network, MobileNetv3-ECA-SPPF, is proposed. This network is capable of reducing the model parameter redundancy and improving the detection speed. The Slim-Neck module is employed to enhance the feature fusion component of YOLOv8s, thereby reducing the computational load and network complexity while maintaining model accuracy. For the precise recognition of small targets such as breakages and flash contamination, the wise intersection over the union v3 edge loss function is introduced to replace the original distance intersection over the union edge loss function, which is more beneficial for the model's efficiency in recognizing small targets. Experimental results demonstrate that L-YOLOv8s can accurately and swiftly identify various types of insulators and their defects. The model's accuracy reaches 94.8%, with a recognition speed of 454.55 frames per second. The number of model parameters is only 3.37M, and the floating-point operation is 7.2 Giga floating-point operations per second. Compared with the YOLOv8 model, the accuracy and floating-point operation of L-YOLOv8s are higher by 2.5%, the recognition speed is improved by 59.09%, the number of model parameters is reduced by 69.69%, and the floating-point operation is decreased by 74.65%. When compared with several traditional models, L-YOLOv8s proves to be practically significant in the identification of insulators and their defects. [ABSTRACT FROM AUTHOR]

Winter jujube is a popular fresh fruit in China for its high vitamin C nutritional value and delicious taste. In terms of winter jujube object detection, in machine learning research, small size jujube fruits could not be detected with a high accuracy. Moreover, in deep learning research, due to the large model size of the network and slow detection speed, deployment in embedded devices is limited. In this study, an improved Yolov5s (You Only Look Once version 5 small model) algorithm was proposed in order to achieve quick and precise detection. In the improved Yolov5s algorithm, we decreased the model size and network parameters by reducing the backbone network size of Yolov5s to improve the detection speed. Yolov5s’s neck was replaced with slim-neck, which uses Ghost-Shuffle Convolution (GSConv) and one-time aggregation cross stage partial network module (VoV-GSCSP) to lessen computational and network complexity while maintaining adequate accuracy. Finally, knowledge distillation was used to optimize the improved Yolov5s model to increase generalization and boost overall performance. Experimental results showed that the accuracy of the optimized Yolov5s model outperformed Yolov5s in terms of occlusion and small target fruit discrimination, as well as overall performance. Compared to Yolov5s, the Precision, Recall, mAP (mean average Precision), and F1 values of the optimized Yolov5s model were increased by 4.70%, 1.30%, 1.90%, and 2.90%, respectively. The Model size and Parameters were both reduced significantly by 86.09% and 88.77%, respectively. The experiment results prove that the model that was optimized from Yolov5s can provide a real time and high accuracy small winter jujube fruit detection method for robot harvesting.

As one of the most important economic crops, grapes have attracted considerable attention due to their high yield, rich nutritional value, and various health benefits. Identifying grape bunches is crucial for maintaining the quality and quantity of grapes, as well as managing pests and diseases. In recent years, the combination of automated equipment with object detection technology has been instrumental in achieving this. However, existing lightweight object detection algorithms often sacrifice detection precision for processing speed, which may pose obstacles in practical applications. Therefore, this thesis proposes a lightweight detection method named YOLOv8s-grape, which incorporates several effective improvement points, including modified efficient channel attention (MECA), slim-neck, new spatial pyramid pooling fast (NSPPF), dynamic upsampler (DySample), and intersection over union with minimum point distance (MPDIoU). In the proposed method, MECA and NSPPF enhance the feature extraction capability of the backbone, enabling it to better capture crucial information. Slim-neck reduces redundant features, lowers computational complexity, and effectively reuses shallow features to obtain more detailed information, further improving detection precision. DySample achieves excellent performance while maintaining lower computational costs, thus demonstrating high practicality and rapid detection capability. MPDIoU enhances detection precision through faster convergence and more precise regression results. Experimental results show that compared to other methods, this approach performs better in the grapevine bunch detection dataset and grapevine bunch condition detection dataset, with mean average precision (mAP50-95) increasing by 2.4% and 2.6% compared to YOLOv8s, respectively. Meanwhile, the computational complexity and parameters of the method are also reduced, with a decrease of 2.3 Giga floating-point operations per second and 1.5 million parameters. Therefore, it can be concluded that the proposed method, which integrates these improvements, achieves lightweight and high-precision detection, demonstrating its effectiveness in identifying grape bunches and assessing biophysical anomalies., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yang and Qiu.)

Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation.

Introduction: Accurate detection of potato seedlings is crucial for obtaining information on potato seedlings and ultimately increasing potato yield. This study aims to enhance the detection of potato seedlings in drone-captured images through a novel lightweight model., Methods: We established a dataset of drone-captured images of potato seedlings and proposed the VBGS-YOLOv8n model, an improved version of YOLOv8n. This model employs a lighter VanillaNet as the backbone network in-stead of the original YOLOv8n model. To address the small target features of potato seedlings, we introduced a weighted bidirectional feature pyramid network to replace the path aggregation network, reducing information loss between network layers, facilitating rapid multi-scale feature fusion, and enhancing detection performance. Additionally, we incorporated GSConv and Slim-neck designs at the Neck section to balance accuracy while reducing model complexity., Results: The VBGS-YOLOv8n model, with 1,524,943 parameters and 4.2 billion FLOPs, achieves a precision of 97.1%, a mean average precision of 98.4%, and an inference time of 2.0ms. Comparative tests reveal that VBGS-YOLOv8n strikes a balance between detection accuracy, speed, and model efficiency compared to YOLOv8 and other mainstream networks. Specifically, compared to YOLOv8, the model parameters and FLOPs are reduced by 51.7% and 52.8% respectively, while precision and a mean average precision are improved by 1.4% and 0.8% respectively, and the inference time is reduced by 31.0%., Discussion: Comparative tests with mainstream models, including YOLOv7, YOLOv5, RetinaNet, and QueryDet, demonstrate that VBGS-YOLOv8n outperforms these models in terms of detection accuracy, speed, and efficiency. The research highlights the effectiveness of VBGS-YOLOv8n in the efficient detection of potato seedlings in drone remote sensing images, providing a valuable reference for subsequent identification and deployment on mobile devices., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Wang, Yang, Liu, Yang, Feng, Sun and Li.)

Introduction: Acupoint localization is integral to Traditional Chinese Medicine (TCM) acupuncture diagnosis and treatment. Employing intelligent detection models for recognizing facial acupoints can substantially enhance localization accuracy., Methods: This study introduces an advancement in the YOLOv8-pose keypoint detection algorithm, tailored for facial acupoints, and named YOLOv8-ACU. This model enhances acupoint feature extraction by integrating ECA attention, replaces the original neck module with a lighter Slim-neck module, and improves the loss function for GIoU., Results: The YOLOv8-ACU model achieves impressive accuracy, with an mAP@0.5 of 97.5% and an mAP@0.5-0.95 of 76.9% on our self-constructed datasets. It also marks a reduction in model parameters by 0.44M, model size by 0.82 MB, and GFLOPs by 9.3%., Discussion: With its enhanced recognition accuracy and efficiency, along with good generalization ability, YOLOv8-ACU provides significant reference value for facial acupoint localization and detection. This is particularly beneficial for Chinese medicine practitioners engaged in facial acupoint research and intelligent detection., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yuan, Shao, Li, Wang, Zhu, Qiu, Chen, Tang and Han.)