A Holistic Segmentation Method for Faulty Electrical Equipment under Complex Background

A method of positioning and integral segmentation of faulty equipment in infrared images acquired during the process of infrared monitoring of electrical equipment in substations with complex backgrounds is proposed to contribute to solving problems including inaccurate positioning and difficult segmentation of faulty equipment. First, the image was segmented using the SLIC superpixel algorithm and the superpixel block was transformed into the Lab color space. The faulty area was obtained after the fault was determined based on the threshold value. Second, relatively bright spots with the maximum connectivity in the image, including faulty equipment, were selected as the original seeds. The number of seeds was controlled based on the principle of maximum variance between classes. Accordingly, primary equipment was obtained using an improved regional growth method. Finally, the overall segmentation of the faulty electrical equipment was completed through an intersection calculation between the faulty area and the main equipment. The results show that the positioning and overall segmentation of faulty electrical equipment under complex backgrounds can be successfully completed using the proposed method. Compared with other segmentation methods, identification of faulty electrical equipment using this method is more complete and accurate.