Research on insulator fault detection based on deep learning

Addressing the challenges posed by the complexity of the background in insulator images captured by drones during power system line inspection, the relatively small proportion of insulator faults in the images, and the blurring effects caused by weather and environmental conditions, this paper proposes improvements to the YOLOv5s detection model. Based on the enhanced detection model, a simulation study of insulator fault detection is conducted. By incorporating Dynamic Snake Convolution (DSC) into the network backbone and adding the GAM (Global attention mechanism) attention mechanism at the bottleneck of the network, as well as utilizing EIOU as the loss function calculation method, three approaches are employed to enhance YOLOv5s. Experimental analysis is conducted using Python, and the results demonstrate that the proposed improvements achieve a 2.1% increase in mean Average Precision (mAP) and a 1.1% increase in recall compared to the original YOLOv5s algorithm, while reducing the model parameters by 5.3%. This enhancement scheme ensures both detection accuracy and model size reduction, promising significant prospects for deployment and application in later stages of the model.