TL-YOLO: Foreign-Object Detection on Power Transmission Line Based on Improved Yolov8

Shao, Yeqin; Zhang, Ruowei; Lv, Chang; Luo, Zexing; Che, Meiqin

doi:10.3390/electronics13081543

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…By comparing their performances on the same dataset, we aim to validate whether the new algorithm can achieve improvements in efficiency, accuracy, and other metrics. The experiment selected Faster-RCNN [36], SSD, YOLOv5s [37], YOLOv7-tiny, YOLOv8s [38], YOLOv9-C [39], and the improved YOLOv8 for comparison. Table 3 showcases the comparative outcomes.…”

Section: Comparison Experiments Of Mainstream Algorithmsmentioning

confidence: 99%

Road Surface Defect Detection Algorithm Based on YOLOv8

Sun,

Zhu,

Qin

et al. 2024

Electronics

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In maintaining roads and ensuring safety, promptly detecting and repairing pavement defects is crucial. However, conventional detection methods demand substantial manpower, incur high costs, and suffer from low efficiency. To enhance road maintenance efficiency and reduce costs, we propose an improved algorithm based on YOLOv8. Our method incorporates several key enhancements. First, we replace conventional convolutions with a module composed of spatial-to-depth layers and nonstrided convolution layers (SPD-Conv) in the network backbone, enhancing the capability of recognizing small-sized defects. Second, we replace the neck of YOLOv8 with the neck of the ASF-YOLO network to fully integrate spatial and scale features, improving multiscale feature extraction capability. Additionally, we introduce the FasterNet block from the FasterNet network into C2f to minimize redundant computations. Furthermore, we utilize Wise-IoU (WIoU) to optimize the model’s loss function, which accounts for the quality factors of objects more effectively, enabling adaptive learning adjustments based on samples of varying qualities. Our model was evaluated on the RDD2022 road damage dataset, demonstrating significant improvements over the baseline model. Specifically, with a 2.8% improvement in mAP and a detection speed reaching 43 FPS, our method proves to be highly effective in real-time road damage detection tasks.

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Section: Comparison Experiments Of Mainstream Algorithmsmentioning

confidence: 99%

Road Surface Defect Detection Algorithm Based on YOLOv8

Sun,

Zhu,

Qin

et al. 2024

Electronics

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Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m

Guarnido-Lopez,

Ramirez-Agudelo,

Denimal

et al. 2024

Animals

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This study highlights the importance of monitoring cattle feeding behavior using the YOLO algorithm for object detection. Videos of six Charolais bulls were recorded on a French farm, and three feeding behaviors (biting, chewing, visiting) were identified and labeled using Roboflow. YOLOv8 and YOLOv10 were compared for their performance in detecting these behaviors. YOLOv10 outperformed YOLOv8 with slightly higher precision, recall, mAP50, and mAP50-95 scores. Although both algorithms demonstrated similar overall accuracy (around 90%), YOLOv8 reached optimal training faster and exhibited less overfitting. Confusion matrices indicated similar patterns of prediction errors for both versions, but YOLOv10 showed better consistency. This study concludes that while both YOLOv8 and YOLOv10 are effective in detecting cattle feeding behaviors, YOLOv10 exhibited superior average performance, learning rate, and speed, making it more suitable for practical field applications.

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Power Transmission Lines Foreign Object Intrusion Detection Method for Drone Aerial Images Based on Improved YOLOv8 Network

Sun,

Shen,

et al. 2024

Drones

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With the continuous growth of electricity demand, the safety and stability of transmission lines have become increasingly important. To ensure the reliability of power supply, it is essential to promptly detect and address foreign object intrusions on transmission lines, such as tree branches, kites, and balloons. Addressing the issues where foreign objects can cause power outages and severe safety accidents, as well as the inefficiency, time consumption, and labor-intensiveness of traditional manual inspection methods, especially in large-scale power transmission lines, we propose an enhanced YOLOv8-based model for detecting foreign objects. This model incorporates the Swin Transformer, AFPN (Asymptotic Feature Pyramid Network), and a novel loss function, Focal SIoU, to improve both the accuracy and real-time detection of hazards. The integration of the Swin Transformer into the YOLOv8 backbone network significantly improves feature extraction capabilities. The AFPN enhances the multi-scale feature fusion process, effectively integrating information from different levels and improving detection accuracy, especially for small and occluded objects. The introduction of the Focal SIoU loss function optimizes the model’s training process, enhancing its ability to handle hard-to-classify samples and uncertain predictions. This method achieves efficient automatic detection of foreign objects by comprehensively utilizing multi-level feature information and optimized label matching strategies. The dataset used in this study consists of images of foreign objects on power transmission lines provided by a power supply company in Jilin, China. These images were captured by drones, offering a comprehensive view of the transmission lines and enabling the collection of detailed data on various foreign objects. Experimental results show that the improved YOLOv8 network has high accuracy and recall rates in detecting foreign objects such as balloons, kites, and bird nests, while also possessing good real-time processing capabilities.

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TL-YOLO: Foreign-Object Detection on Power Transmission Line Based on Improved Yolov8

Cited by 4 publications

References 38 publications

Road Surface Defect Detection Algorithm Based on YOLOv8

Road Surface Defect Detection Algorithm Based on YOLOv8

Programming and Setting Up the Object Detection Algorithm YOLO to Determine Feeding Activities of Beef Cattle: A Comparison between YOLOv8m and YOLOv10m

Power Transmission Lines Foreign Object Intrusion Detection Method for Drone Aerial Images Based on Improved YOLOv8 Network

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