Infrared Image Target Detection Algorithm for Hardware Fittings at Tension Clamp of Qishao UHV Transmission Line

The infrared thermal imager is a common method for electric inspection to detect the abnormal heat of the gold tool at the tensioning wire clamp. Because the Gansu section of Qishao High voltage line accounts for about 52.4% of the total length, through a lot of complex geographical environment, even once a quarter manual inspection will cost time and effort, subjectivity is big, and real-time performance is poor. Aiming at the above, this paper proposes an improved YOLOv4 multi-target recognition algorithm for infrared images. Firstly, the backbone feature extraction network CSPDaeknet53 is replaced by Mobilenetv1. Secondly, the training dataset is increased by the base hjs image expansion technology. Finally, Mosaic data enhancement and cosine annealing attenuation technology are introduced to construct an effective infrared image detection model to realize the classification and recognition of the hardware. The experimental results are compared with SSD, YOLOv3 and YOLOv4 target detection algorithms, and it is found that the average recognition accuracy of the proposed method is 93.84%, and the model size is only 55.53MB. Under the condition of ensuring accuracy, the model is smaller and has practical reference value.