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The faults caused by insulator defects will seriously threaten the operational safety of the power grid. Therefore, insulator defect detection play a crucial role in inspecting transmission lines. Compared with traditional methods, the network such as You Only Look Once (YOLO) family based on deep learning have high accuracy and strong robustness in insulator recognition and fault detection. However, the performance of these network are usually affected by the shooting conditions as well as aerial images with diverse types of insulators and complex backgrounds, resulting in poor detection result. In addition, the relatively small insulator fault (bunch-drop) area in aerial images will also make detection difficult. To solve these problems, this paper proposes an improved insulator defect detection model based on YOLOv4 (ID-YOLO). To create our model, we design a new backbone network structure, Cross Stage Partial and Residual Split Attention Network (CSP-ResNeSt), that can solve the interference problem of complex backgrounds in aerial images to enhance the network’s feature extraction capability. In addition, we adopt a new multiscale Bidirectional Feature Pyramid Network with Simple Attention Module (Bi-SimAM-FPN), which can address the difficulty of identifying a small scale of insulator defects in an image for more efficient feature fusion. We experimentally demonstrate that the mean average precision (mAP) of the proposed model is 95.63%, which is 3.5% higher than that of the YOLOv4. Most importantly, the detection speed of this model can reach 63 FPS, which meets the requirements of real-time detection of insulator bunch-drop faults.
Hao et al. (Sat,) studied this question.