Deep Learning-based UAV Visual Recognition System in Distribution Network Defect Detection

Distribution line project acceptance is a key link in the quality control of distribution line grid project, which is an important factor affecting the safe and stable operation of distribution network and directly determines the level of safe operation of distribution network. In order to solve the problem of long distribution network lines, complex and large amount of equipment, manual acceptance is time-consuming and laborious, and to improve the efficiency of field work and scientific decision-making ability. In this paper, we propose a small target inspection network CMH-YOLOv5 based on CBAM attention mechanism and tiny target detection head. Compared with the original YOLOv5, the main improvements are the use of attention mechanism in the shallow layer of the network and the addition of a tiny target detection head to introduce the attention module to improve the performance of target detection. Optimization improves the efficiency of inspection work, provides empirical evidence for the engineering turn-around sequence, improves the field efficiency and scientific decision-making ability, and realizes the digital transformation of UAV operation in the direction of power system infrastructure engineering.