Application of Computer Vision Technology in Defect Detection of Electrical Engineering Equipment and Strategies for Improving Recognition Accuracy

Abstract

Addressing the limitations of traditional electrical equipment defect detection methods, including low efficiency, high missed detection rates, strong subjectivity, and insufficient real-time performance, this paper investigates the application of computer vision technology and corresponding strategies for improving recognition accuracy. Reliable defect identification is essential for ensuring the operational safety and electromagnetic reliability of modern power equipment and intelligent electromagnetic infrastructures. First, based on image processing, deep learning, and pattern recognition theories, a computer vision framework for electrical equipment defect detection is established, covering image acquisition, preprocessing, feature extraction, and defect classification. Second, through field investigations and experimental analysis, the visual characteristics of typical defects in transformers, circuit breakers, and insulators are systematically analyzed, while key challenges including complex environmental interference, limited small-sample recognition capability, and inadequate feature extraction are identified. Finally, an experimental platform is constructed using three representative categories of electrical engineering equipment, and comparative experiments are conducted to evaluate multiple optimization strategies. The results demonstrate that the proposed approach effectively improves defect recognition performance and provides a practical solution for intelligent electromagnetic equipment inspection, condition monitoring, and reliability enhancement in advanced power systems.