Robust defect detection in 2D images printed on 3D micro-textured surfaces by multiple paired pixel consistency in orientation codes

Abstract

Defect detection is now an active research area for production quality assurance. Traditional visual inspection systems are developed by human beings, which is a time-consuming, labour-intensive, and highly error-prone process, and are therefore unreliable. To overcome these problems, the authors proposed a new method for detecting defects when printing on a 3D micro-textured surface. They utilise an orientation code as the basis to resist the fluctuations in illumination. Based on the consistency of the pixel pairs, they developed a model called multiple paired pixel consistency to represent the statistical relationship between each pixel pair in defect-free images. Finally, based on this model, they designed a defect detection method. Even with different defect sizes, illumination conditions, noise intensities, and other characteristics, the performance of the proposed algorithm is extremely stable and highly accurate, and the recall, precision, and F-measure in most of the results can reach 0.85,0.93, and 0.9, respectively. In addition, the defect detection rate can reach almost 100%. This demonstrates that the authors' approach can achieve state-of-the-art accuracy in real industrial applications.