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Insulators play an important role in supporting and securing live conductors. In the task of insulator fault detection, existing models often miss small targets due to the interference of complex backgrounds. Furthermore, due to the very large aspect ratio of the insulators themselves, most models contain extraneous background in their results. To solve the above problems, this paper makes full use of the characteristics of the insulator itself and optimizes YOLOv5 from four aspects: feature extraction, feature fusion, candidate frame generation, and loss function. We divide the image feature extraction process into two procedures. The first procedure is mainly responsible for extracting insulator images from complex backgrounds, and the second stage is based on the extracted insulator images for fault analysis. At the same time, considering the trouble that the objective is too small due to the shooting distance, we design an attention mechanism that is more suitable for practical needs to enhance the ability to capture small objects. Furthermore, we design a rotated candidate box to improve the quality of the predicted box and reduce irrelevant background. The FAINdetection dataset is compared with various advanced detection methods. The proposed method is more effective than the previous model by verifying the self-made data. Notably, the model achieved an average accuracy of 98% at 29 frames per second (FPS), laying the foundation for automatic insulator fault detection.
Ming et al. (Sun,) studied this question.