Performance Evaluation of Contouring Algorithms for Detecting Chipping Defects in Automobile Coatings

This study presents an efficient and scalable algorithm for detecting and grading chipping defects in automotive coatings, addressing the challenges of subjective and inconsistent manual inspections. By leveraging advanced preprocessing techniques such as grayscale conversion, top-hat filtering, and contrast enhancement, the proposed method enhances image quality and highlights subtle defects. Contour detection techniques, combined with mathematical models, ensure precise boundary identification, feature extraction, and grading based on PSA standards. Tested on metallic white and black coatings, the algorithm demonstrated high accuracy and robustness across diverse textures. This scalable solution offers significant potential for the automotive industry, with future improvements aimed at integrating machine learning for enhanced adaptability and precision.

کلیدواژه ها:

Image Processing ، Chipping Defects ، Automotive Coatings ، Contour Detection ، Quality Control