国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Infrared detection can detect temperature anomalies in substation power equipment and reduce the probability of safety accidents. Therefore, an improved CenterNet object detection algorithm model, CenterNet, is proposed_ PRO. This algorithm adopts ShuffleNet V1/V2 as the backbone network and introduces FPN to extract multi-scale features. In order to overcome the difficulties of target detection at different scales, increase rotation angle regression branches, predict the rotation angle of the target, and optimize with improved IoU Loss, further improving the model detection speed and accuracy. The surface temperature of power equipment is extracted by the threshold segmentation method and analyzed and calculated, and the temperature defect judgment specification and temperature warning threshold of power equipment are designed and formulated, and the related defects of power equipment can be judged according to the specification. The experimental results show that the average accuracy of the improved CenterNet model reaches 90%. Compared with the traditional CenterNet model, the average accuracy is improved by 1.3 percentage points, which can meet the high requirements for infrared detection of power equipment in the actual substation scenario.