Detection of Insulator Defects With Improved ResNeSt and Region Proposal Network

Insulator is an important part of transmission line. Defective insulators will cause potential safety hazard to transmission lines. Image detection technology can improve the efficiency of insulator defect detection and greatly reduce the maintenance cost. However, the existing insulator defect detection technology has the disadvantages of low accuracy and long detection time. An insulator defect detection method based on improved ResNeSt and Region Proposal Network (RPN) was proposed. First, this method builds a new network based on ResNeSt. Secondly, we added the improved RPN to the improved ResNeSt for feature extraction, to better detect minor defects on insulators. Finally, we enhanced the data processing and labeled the open insulator data set. On this data set, the proposed model is tested and a large number of controlled experiments are done. The results show that the proposed network is more accurate and faster than the control group. Moreover, the proposed network has an accuracy rate of 98.38% for insulator defect detection, which can detect 12.8 pictures per second. The proposed method has good efficiency and practicability in aerial photo insulator defect detection.