Defect Detection of Power Equipment by Infrared Image - Ruiyong HUANG; Meisheng DAI; Yuebin ZHENG; Qinqin HUANG; Liye KANG; Xiantai GOU; Weichao ZHOU (Zhongguo dianli)
The infrared image collected by the robot during inspection is hard to reflect the texture information of the equipment target. The artificial methods or traditional machine learning methods cannot accurately identify and classify the defects of power equipment, and other environmental factors may easily lead to false judgment. In this paper, the algorithm model of CenterNet combined with structured positioning is adopted. Through collecting field infrared image data samples, the algorithm model is trained and verified to identify and position different substation equipment and its components with high accuracy from complex infrared images. According to the surface temperature range of equipment components and the type of substation equipment, the infrared image is combined with relevant temperature specifications to realize the defect detection of power equipment. The experimental results show that this method improves the accuracy of infrared image for detecting the defects of power equipment, and provides a new idea for infrared image used for intelligent detection of power equipment.