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Machine vision detects and grades chipping in automotive coatings
Researchers have developed an automated machine vision system for the objective detection and grading of chipping defects on automotive coatings. The method delivers consistent results, correlates strongly with expert inspectors and cuts inspection time roughly sevenfold.
Chipping is a common surface defect in automotive coatings, caused by high-velocity impacts from small stones and gravel that lead to localised detachment of the coating. Conventional evaluation of chipping resistance relies on visual inspection according to industry standards, an approach that is subjective and prone to inconsistency between inspectors. A recent study addresses this shortcoming by developing an automated machine vision and image processing method for the objective detection and grading of chipping damage.
Coated panels were prepared and tested in accordance with the PSA D241312-H (Peugeot) standard to obtain varying degrees of chipping. High-resolution images were subsequently acquired under uniform illumination to ensure comparable input data. The image processing pipeline combined contrast enhancement, thresholding and contour-based analysis to identify and classify damaged areas as a function of chip size and chip distribution.
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Objective grading and significant time savings
The system was able to detect even subtle and complex defects that were often difficult to identify by human observers, delivering consistent and reproducible grading. In contrast, visual evaluations performed by four experienced inspectors showed substantial variability, reflected by large standard deviations across samples. This confirms the known limitations of relying purely on subjective inspection for quality control tasks in coatings.
Despite the observed disagreement between inspectors, the automated method achieved a strong correlation with their mean ratings, with a coefficient of R = 0.86. This underlines both the accuracy of the vision-based approach and its capacity to provide a more stable and objective reference. In addition, the automated method reduced inspection time by approximately a factor of seven compared with manual evaluation. According to the authors, the approach offers a practical tool for quality control in automotive coating production and can support more consistent decisions on coating performance and process optimisation.
Source: Afshargoli, K. et al., A new approach to detect and grade the chipping defect of automotive coatings using a machine vision system. Journal of Coatings Technology and Research 23, 2791–2803 (2026).
