Detection of key components of transmission lines defect identification studies

Due to the fact that the transmission line is in the outdoor environment for a long time, it is easy to be affected by bad weather, environmental pollution and other factors, resulting in defects in line equipment, which may lead to accidents. This section focuses on the identification of defects in key components of transmission lines. The transmission line targets were extracted, and the SSD algorithm and the YOLO target detection algorithm were combined with the strategy, and the defects of insulators, shockproof hammers and spacers were identified into the improved YOLOv5 algorithm network, and the detection model was evaluated through the evaluation index. Compared with the mainstream algorithms, and ablation experiments were carried out, it was found that we propose an improvedYOLOv5 algorithm. By incorporating K-means++ adaptive anchor box optimization, we enhance the model's adaptability to target size variations, resulting in a 3.7% mAP improvement. We design the Gating Layer Block to better capture contextual information of small target defects, enhancing the network's receptive field and feature extraction capability, leading to a 1.9% mAP improvement. We introduce the CSAtt attention mechanism module to broaden the model's focus range. We also design the Spatially Adaptive Convolution with variable dilation convolution to adapt to objects of different scales, resulting in a 9.1% precision improvement. Finally, we improve the polynomial loss function to enhance model performance and save computational resources and time.