Research on Front-end Electrical Component Recognition Algorithm based on Improved Yolov5s

With the continuous expansion of transmission line scale, line inspection has faced great challenges. To solve the problems of high labor intensity, low efficiency, and difficult working conditions in traditional manual inspection, autonomous inspection based on unmanned aerial vehicle (UAV) came into being. In this paper, an electrical component recognition algorithm based on improved yolov5s is proposed. Self-calibrated convolutions (SCConv) is introduced to improve the feature extraction capability. The Gamma transform is used to preprocess the image set, which can improve the robustness of the algorithm to overexposed and underexposed scenarios. The experimental results show that the proposed algorithm can be used for real-time electrical component recognition at the front end of UAV. The recognition accuracy of various typical components can reach more than 98%, which can significantly improve the acquisition quality of inspection images and lay a good foundation for further data processing at the back end of inspection system.