A New Method for Rapid Detection of Surface Defects on Complex Textured Tiles

Abstract

The surface of complex textured ceramic tiles contains numerous defects that exhibit low contrast with the background, making them easily confused with the textured background during detection. Traditional defect detection algorithms and convolutional neural networks are prone to texture interference in the defect detection of complex textured ceramic tiles, resulting in high false detection rates and missed detection rates. Inspired by the human eye’s ability to find surface defects on smooth objects against a high-light background, this paper proposes a new method for detecting surface defects of complex textured tiles. This method uses the high-light area generated by the reflection of the light source as the background for detecting textured tile defects, thereby increasing the threshold difference between the defect and the background and highlighting the defect. This method translates the position of the textured tiles horizontally and captures images while the reflection of the strip light source covering the surface of the tiles is in motion, thereby acquiring several tile images with light source reflections. Subsequently, after intercepting the images of the highlight areas covered by the light source reflection, the RANSAC algorithm is used to match the characteristic corners of these images, and after rigid splicing, a complete image of the textured tiles with the highlight area as the background is obtained. Finally, defects on textured tiles can be extracted through threshold segmentation and morphological filtering. Experimental results indicate that this method can ignore complex texture interference on ceramic tiles and achieve rapid detection of defects in textured ceramic tiles.