Electrical Equipment External Defect Detection Based on Lightweight YOLOv5
摘要
In order to improve the accuracy of real-time detection of external defects of electrical equipment in sub stations and make the detection model more lightweight, a lightweight YOLOv5 based external defect detection method for electrical equipment was proposed. Firstly, the external defect image dataset of electrical equipment was constructed and processed by data enhancement. Secondly, three optimization strategies were used to improve the original YOLOv5. The EfficientViT network was introduced to improve the backbone network of the algorithm to re duce the number of model parameters, and the SimAM parameter-free attention mechanism was added to the Neck part of the algorithm to improve the recognition accuracy with the complex background of the substation. At the same time, the Soft-NMS module was used to improve the screening method of the detection box to avoid the phe nomenon of defect missed detection. Finally, verified by ablation test, the mAP value of the lightweight external de fect detection model of electrical equipment was stable at 86.4%, which was 1.2 percentage points higher than that of the original model, the number of model parameters were reduced by 20%, the calculation amount was reduced by 38%, and the model size was 11 MB, which was 19.7% lower than that of the original model. The improved model could meet the requirements of real-time detection of external defects of equipment.