Abstract:This article provides a comprehensive analysis of the inspection process and technical support measures for secondary power equipment by applying machine vision technology, exploring the feasibility of achieving automated inspections. The research involves conducting automated inspections based on the analysis results of equipment images, starting with capturing inspection images of the equipment, followed by analyzing and preprocessing image features using computer vision technology. After noise reduction and purification treatments, a regional distributed detection model was established, laying the foundation for inspections. On this basis, the study used a local minimum filter template to deeply analyze the inspection principles and perform a three-dimensional reconstruction of the inspection scenes. A variable structure distribution model constructed by gradient descent method centered around edge pixel points at high gray value positions successfully achieved visual reconstruction recognition of secondary power equipment, leading to the formation of an automated management and control mechanism. Simulation experiments verified the effectiveness of the proposed method, demonstrating that it can not only quickly and accurately complete feature recognition but also precisely guide the automatic inspection process. This achievement provides new technical support for the automated inspection of secondary power equipment and showcases the potential applications of machine vision in this field.