Volume 40, Number 2, April 2022
|Page(s)||414 - 421|
|Published online||03 June 2022|
Research on defect detection algorithm of complex texture ceramic tiles based on visual attention mechanism
State Key Laboratory of High Performance and Complex Manufacturing, Central South University, Changsha 410083, China
2 College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China
Aiming at the difficulty of detecting the surface defects of complex texture tiles, a salient target detection method based on the human visual attention mechanism is proposed and used for the detection of tile surface defects. Firstly, the image of ceramic tile surface is pretreated using the single-scale SSR light correction method and bilateral filtering method; Secondly, according to the principle of contrast and high-frequency suppression in the visual attention mechanism, aiming at the "imaging" and "aggregation" characteristics of complex background textures, a detection model based on the visual attention mechanism is established to determine and mark defects.According to the contrast principle and high-frequency suppression principle in visual attention mechanism, feature extraction of ceramic tile surface is carried out. Then, the image color patch weight salient map and image feature fused salient map are obtained, and the two maps are fused according to the image saliency criteria.Finally, the marked ceramic tile defects are determined and marked.Finally the marked ceramic tile defects are obtained. This defect detection algorithm and the other two algorithms are applied to three kinds of randomly selected complex texture ceramic tiles. The experimental results show that compared with other algorithms, our algorithm can achieve a comprehensive detection rate of more than 96% for complex texture ceramic tiles, and can obtain a good effect of ceramic tile defect detection as well.
Key words: defect detection / salient object Detection / illumination correction / complex texture / visual attention mechanism
关键字 : 缺陷检测 / 显著性目标检测 / 光照校正 / 复杂纹理 / 视觉注意机制
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