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论文:2022,Vol:40,Issue(2):414-421 |
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引用本文： |
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欧阳周, 张怀亮, 唐子暘, 彭玲, 俞胜. 复杂纹理瓷砖表面缺陷检测算法研究[J]. 西北工业大学学报 |
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OUYANG Zhou, ZHANG Huailiang, TANG Ziyang, PENG Ling, YU Sheng. Research on defect detection algorithm of complex texture ceramic tiles based on visual attention mechanism[J]. Northwestern polytechnical university |
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| 复杂纹理瓷砖表面缺陷检测算法研究 |
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| 欧阳周1,2, 张怀亮1,2, 唐子暘1,2, 彭玲1,2, 俞胜1,2 |
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1. 中南大学 高性能复杂制造国家重点实验室, 湖南 长沙 410083;
2. 中南大学 机电工程学院, 湖南 长沙 410083 |
| 摘要: |
| 针对复杂纹理瓷砖表面缺陷检测困难的问题，提出一种基于人眼视觉注意机制的显著性目标检测方法并用于瓷砖表面缺陷检测。利用单尺度SSR光照校正方法和双边滤波方法对图像进行预处理;根据视觉注意机制中的对比度原理及高频抑制原理，针对复杂背景纹理的“成像性”与“聚集性”特征，建立基于视觉注意机制的检测模型，根据视觉注意机制中的对比性原理和高频抑制原理对瓷砖表面进行特征提取，再依据图像的显著性准则得到图像颜色斑块权重显著图和图像特征融合显著图并将两者融合，进行缺陷的判定和标记，最终得到已标记的瓷砖缺陷。将此缺陷检测算法和另外2种算法应用于随机选取的3类复杂纹理瓷砖并进行对比实验，结果表明，相比较于其他算法，此算法对复杂纹理瓷砖的缺陷检测达到96%以上的综合检测率，可以获得良好的瓷砖缺陷检测效果。 |
| 关键词:
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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| Research on defect detection algorithm of complex texture ceramic tiles based on visual attention mechanism |
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| OUYANG Zhou1,2, ZHANG Huailiang1,2, TANG Ziyang1,2, PENG Ling1,2, YU Sheng1,2 |
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1. 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 |
| Abstract: |
| 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
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| 收稿日期: 2021-07-22
修回日期:
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| DOI: 10.1051/jnwpu/20224020414 |
| 基金项目: 国家重点基础研究发展计划（2013CB035400）资助 |
| 通讯作者: 张怀亮(1964-),中南大学教授,主要从事机器视觉和液压系统动力学研究。e-mail:zhl2001@csu.edu.cn
Email：zhl2001@csu.edu.cn |
| 作者简介: 欧阳周(1996-),中南大学硕士研究生,主要从事机器视觉研究。
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| 作者相关文章 |
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| 欧阳周 在本刊中的所有文章 |
| 张怀亮 在本刊中的所有文章 |
| 唐子暘 在本刊中的所有文章 |
| 彭玲 在本刊中的所有文章 |
| 俞胜 在本刊中的所有文章 |
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