Research on infrared recognition of power equipment based on improved YOLOV4-tiny
Infrared detection technology is widely used in the field of overheat fault diagnosis of power equipment because of its advantages such as no direct contact,live detection and fast detection.However,the poor infrared image quality and the complex distribution of power equipment have an adverse effect on the detection accuracy of power equipment type in the process of power equipment fault detection.In order to realize the fast and accurate identification of power equipment type,this paper proposes a YOLOV4-tiny object detection model suitable for the identification of power equipment type in the process of overheat fault diagnosis based on the YOLOV 4-tiny object detection algorithm.The original detection model is im-proved by replacing the horizontal rectangular frame mechanism with the rotating rectangular frame mechanism,improving the activation function and using PAN+FPN to strengthen the feature extraction network,so as to make the detection more accu-rate and faster.Through many experiments,the optimized model has no significant improvement in detection speed compared with YOLOV4 and YOLOV4-TINY models,but its detection accuracy is increased by 1.89%.This study has brought a new idea for the equipment type identification in the infrared diagnosis process of power equipment overheating fault.
NETL
NSTL
万方数据
维普
