Application of Deep Learning in Abnormal Insulator Infrared Image Diagnosis

Because of the effects of the background environment and data volume, the accuracy and efficiency of abnormal defects in traditional infrared images of insulators are generally low. In this study, a deep-learning anomaly diagnosis method is proposed. Based on the improved faster region-based convolutional neural network (R-CNN) method, a detection network is built to test different types of insulators. Results show that compared with the back propagation neural network and faster R-CNN methods, the proposed method can diagnose abnormal defects of insulators efficiently with a mean average precision of 90.2%. In addition, the diagnostic accuracy of single type Ⅰ and type Ⅴ insulators is higher than that of double type Ⅰ insulators. The results can provide a reference for insulator defect identification in transmission lines.