An effective MID-based visual defect detection method for specular car body surface

Abstract

The quality control of car body surfaces is one of the most important tasks in automotive manufacturing, which can directly affect the appearance of cars and purchasing experience of consumers. However, current car body defect detection methods rely mostly on manual inspection, which is subjective, inaccurate, and labor intensive. One of the biggest challenges of automated defect detection is that the specular reflection properties of car body surfaces make obtaining high-quality defect images extremely difficult. This article proposes a novel method called modulated intensity decoding (MID), in which preset encoded fringe patterns are projected onto the surface of a car body, and a camera is used to capture images after the patterns are reflected by the surface. By decoding the captured reflection images, high-quality surface defective decoded (SDD) images are obtained, which preserve the appearance of the defects and effectively highlight the defects against the background. Comparison experiments are performed on detecting and classifying the car body surface defects using four typical object detection networks to demonstrate the effectiveness of the proposed method. The results indicate that our method is far superior to that of conventional approaches using a square-wave-like-pattern light source.