Abstract
The performance of insulator defect detection model is not satisfactory due to the small object size, imbalanced and insufficient data. In this paper, based on YOLOv5 model, we propose an insulator defect detection method incorporating feature fusion and attention mechanism. Firstly, multi-scale feature fusion is introduced to strengthen the ability to extract minute features from images. Secondly, an attention mechanism based on SE-C module is proposed to improve the detection of defective objects. In addition, K-means++ is used to customize anchor boxes to meet the actual requirements and avoid mismatches. The experimental results show that the proposed model achieves 92.4% precision on the public insulator dataset, which demonstrates the applicability of the auto-detection system for insulator defects significantly.
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