The infrared images collected by robots during the inspection process are difficult to reflect the texture information of the equipment. Artificial methods or traditional machine learning methods cannot accurately identify and classify power equipment defects, and other environmental factors can easily lead to misjudgment. In response to this issue, a CenterNet based defect detection method for power equipment is proposed in this paper. This method uses CenterNet and combines structured positioning to collect and train on-site infrared image data samples, achieving high accuracy in identifying and locating different substation equipment and components from complex infrared images. Based on the surface temperature range of equipment components and the identified substation equipment type, combined with relevant temperature specifications, infrared image defect detection of power equipment is achieved. The experimental results show that this method improves the detection accuracy of infrared image defect detection in power equipment.
Substation personnel behavior detection is mainly based on the two-dimensional image taken by the camera for discrimination. The substation has the characteristics of complex environment and multiple types of personnel operations, which leads to the low accuracy of two-dimensional image of personnel behavior discrimination. To solve this problem, this paper studies a human skeleton point detection model based on mask initialization. First, the operator image in the substation is processed by using the joint detection and segmentation network Mask R-CNN to obtain the boundary frame and mask of the human target, and then the target area is detected from bottom to top. Experiments show that this method not only reduces the impact of complex environment and job types on the detection accuracy, but also greatly reduces the invalid convolution operations, thus improving the detection speed.
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