Research on surface defect detection technology of power equipment based on image processing

In power equipment operations, ensuring reliable and safe functioning depends heavily on effective surface defect detection. This paper presents an image-processing-based solution that enhances surface defect detection for electrical equipment by refining the Simple Linear Iterative Clustering (SLIC) algorithm. First, a guided filter is applied to infrared images to improve contrast and clarity while preserving edge information. This preprocessing step facilitates better feature extraction by reducing noise and maintaining important image details. Next, the improved SLIC algorithm segments the filtered images into superpixel blocks with similar characteristics, which helps isolate and precisely mark defect areas on electrical equipment surfaces. By accurately partitioning the image, the method enables clear identification of fault regions, making it more straightforward to diagnose and address defects. This enhanced segmentation approach thus contributes significantly to maintaining the safety and reliability of power equipment under a wide range of operational conditions. On the test set, the method improves the boundary recall rate, and effectively improves the visual effect of generating super pixel shape size, and verifies the effectiveness and feasibility of the method in the surface defect detection of electrical equipment. In summary, the image processing-based surface defect detection technology of power equipment proposed in this study has important application prospects. Through the pretreatment of infrared image and the improved algorithm segmentation, the fault area of electrical equipment can be accurately located and marked, which provides strong support for equipment maintenance and repair. In the future, we will further explore and optimize this method, improve its application effect in the field of power equipment, and promote the development of intelligence and automation of power equipment.