Review of Visual Defect Detection Technology for Micro Coaxial Cable Harnesses

The identification of defects in micro coaxial cable harnesses is of paramount importance in ensuring the quality of the production process. However, the prevailing methodology for detecting such defects in micro coaxial cable harnesses is predominantly manual, which has resulted in poor detection accuracy and precision. Furthermore, this approach had a deleterious effect on both the efficiency and quality of the production process. In order to achieve a rapid and precise defect detection technology, this paper provided a comprehensive analysis of the production line status and the various defect types associated with micro coaxial cable harnesses. Subsequently, we discussed the application of traditional image processing methods, machine learning methods, and deep learning methods to defect detection technology for high-precision components, and summarized the algorithmic performance, advantages and limitations of the various methods. The findings of this paper demonstrated that traditional image processing methods and machine learning methods exhibited inadequate robustness, were susceptible to illumination-related variations, and had a restricted scope of applicability. In contrast, deep learning methods have shown improved detection precision and efficiency, nevertheless, remaining constrained by certain limitations. This paper presents a discussion and proposes the corresponding suggestions from three perspectives: the impact of illumination environment, defect dataset and defect detectability. The objective was to enhance the precision and robustness of visual defect detection, thus establishing a foundation for the subsequent design of visual inspection system for defects in micro coaxial cable harnesses. Furthermore, it furnished pertinent relevant references for the application of high-precision component defect visual inspection.