Detecting dents in car bodies using machine learning and structured light projection

Abstract

This article discusses feasible methods for detecting dents in car bodies caused by transportation damage, commuting collisions, and hail. The authors review existing approaches exploiting their limitations, including smartphone-based ML detection algorithms and drive-through tunnels. The paper details the setup for capturing dents using computer vision with industry-grade cameras and structured light projection, emphasizing optimized data acquisition and computer vision setup. A particular emphasis is placed on acquiring high-quality input data thanks to the proper calibration and alignment of cameras, structured light, and the synchronization between them. Challenges related to obtaining high-quality footage in real-life conditions, such as car speed, body color, and lighting conditions, are thoroughly discussed. The method covers algorithms for detecting car paint, optimizing camera parameters, and identifying dents. Data annotation methods are described in detail, ensuring robust training datasets. Validation of the method is based on comparing the results of an inspection by professional car appraisers with algorithm detection outcomes. The results demonstrate the effectiveness of the proposed methods. Additionally, the article explores future research opportunities, such as scratch detection, damage severity estimation, and integrating these systems into automated production lines. The potential for enhancing vehicle inspection processes through advanced computer vision and structured light techniques is also considered.