Unmanned Aerial Vehicles (UAVs) are increasingly vital in diagnosing insulator defects, essential for ensuring the stability and safety of electrical transmission lines. In response, we propose a lightweight insulator defect detection model suitable for edge computing environments. Initially, we introduce the yolov8-C2f-RepGhost model, which features a streamlined backbone network. The conventional bottleneck in the C2f module of the backbone network is replaced with a RepGhost bottleneck, utilizing structural re-parameterization to enhance efficiency, thus rebranded as C2f-RepGhost. The RepGhost module significantly boosts detection speed. Furthermore, we employed GridMask data augmentation to expand and diversify the dataset, improving its utility in training and enhancing the model’s generalization capabilities. Our experimental results demonstrate that the yolov8-C2f-RepGhost model achieves notable enhancements in both speed and accuracy when trained on this augmented dataset.
Strain clamps of transmission lines are the main part of transmission lines, which play the role of bearing tensile force and transmitting current in the transmission line. In this paper, we focus on the distance between the end of steel anchor and strand in the strain clamps. We propose a method using YOLOv4 and classic image processing methods to measure the distance referred to above, according to which we can judge whether the strain clamps have defects about distance. We first got the bounding boxes of the end of steel anchor and strand in the strain clamps by YOLOv4, thus calculating the distance between the two bounding boxes. Meanwhile, we used classic image processing methods to get the diameter of the steel anchor. Finally, we calculated the ratio of the two distances mentioned above to determine whether the defect exists. We achieve good performance in the detection of distance defects in strain clamps with high accuracy through the research in this paper.
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