Abstract
Abstract The problem of birds building nests on high-altitude towers has posed a significant hidden danger to the safe operation of long-distance transmission lines. The current manual inspection method is inefficient and costly, while automatic inspection technology still faces challenges in accuracy and efficiency. This article mainly aims to propose a deep learning target detection algorithm YOLOV3 based on convolutional neural networks to monitor and retrieve bird damage faults on power towers. By constructing a large dataset of bird nests on power towers, deep learning training models are used to extract features of the detection targets, and the YOLOV3 algorithm is used to intelligentially identify images with bird damage faults. Experiments show that YOLOV3 can effectively detect bird nests on power towers.
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