Design of Infrared Anomaly Detection for Power Equipment Based on YOLOv3

摘要

Power equipment is an important part of the power system and the focus of power system operation and maintenance. Infrared anomaly detection technology is an effective means to detect abnormalities of power equipment because of its safety, simplicity and intuitiveness. Through training the YOLOv3 network by infrared images collected in the field, this work can achieve real-time detection of power equipment and fault points on the Jetson Nano, and determines which areas of the power equipment are abnormal. The trained YOLOv3 model is tested. The mAP value of the model is 34.63%, the recall rate is 21%, and the temperature anomaly area and power equipment could be marked. The running time on the Jetson Nano was 0.7-0.9 s (the recognition time was less than 1s), which satisfies the requirements for power equipment testing.