Key Technologies for Image Recognition of Surface Defects in Substation Equipment

In order to improve the accuracy and efficiency of surface defect recognition in substation equipment, an optimization scheme based on key image recognition technologies is proposed by analyzing the limitations of traditional operation and maintenance inspections. Method: This article uses high-precision professional equipment to collect images of substation equipment, and optimizes image quality through image enhancement technology to reduce environmental interference. As a result, a standardized dataset was constructed and a deep convolutional generative adversarial network(DCGAN) was introduced to train the model, achieving efficient and accurate defect recognition. Conclusion: The experimental results show that the system performs well in classification accuracy and can significantly improve the operation and maintenance management level of substation equipment, providing strong guarantees for the stable operation of the power system.