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Improved YOLOX-S Abnormal Condition Detection for Power Transmission Line Corridors

In transmission line corridors, there are numerous abnormal conditions, such as bird nests, suspended foreign objects, wildfires, and smog, which always affect the normal operation of transmission systems. Considering the various types of foreign objects, complex backgrounds, occlusion, wildfires, irregular smoke shapes, and the real-time requirements of drone detection, this paper introduces an information aggregation algorithm based on YOLOX-S. This algorithm enhances relevant features and suppresses irrelevant features by aggregating spatial information and channel information in the feature map, which raises the overall learning ability of the network to improve the detection accuracy. Finally, the test on the transmission line environmental data set shows that the detection accuracy of this method in abnormal conditions such as bird's nest, hanging foreign body, wildfire and smog is 88.5%, which is 13.9% higher than that before the improvement, and the increase of reasoning time is very small. The experimental results show that this method has made significant progress in the abnormal environmental condition detection of power transmission line corridors.