A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform
A Surface Defect Detection Framework for Glass Bottle Bottom Using Visual Attention Model and Wavelet Transform
复制标题
使用视觉注意力模型和小波变换的玻璃瓶底表面缺陷检测框架
DOI：
10.1109/tii.2019.2935153
复制
发表时间：
2020-04
影响因子：
12.3
通讯作者：
Hui Zhang
中科院分区：
文献类型：
--
作者：
Xianen Zhou;Yaonan Wang;Qing Zhu;Jianxu Mao;Changyan Xiao;Xiao Lu;Hui Zhang
玻璃瓶在用于包装前必须彻底检查。然而，由于定位不准确、纹理区域缺陷检测困难、中心面板亮度本身不均匀等问题，瓶底缺陷视觉检测在质量控制中仍然是一项具有挑战性的任务。为了克服这些问题，我们提出了一个表面缺陷检测框架，该框架由三个主要部分组成。首先，提出了一种新的定位方法——熵率超像素圆检测（ERSCD），该方法将最小二乘圆检测与熵率超像素（ERS）相结合，并改进了随机化圆检测方法，以准确获取瓶底感兴趣区域（ROI）。然后，根据结构属性将感兴趣区域划分为两个测量区域：中央面板区域和环状纹理区域。针对前者，提出了一种频率调谐各向异性扩散超像素分割（FTADSP）缺陷检测方法，该方法将频率调谐显著区检测（FT）、各向异性扩散和改进的超像素分割相结合，以精确检测缺陷的区域和边界。基于小波变换和多尺度滤波算法，提出了一种小波变换多尺度滤波（WTMF）缺陷检测策略，以降低纹理的影响，提高对定位误差的鲁棒性。在我们设计的视觉系统获得的四个数据集上对所提出的框架进行了测试。实验结果表明，与许多传统方法相比，我们的框架取得了最好的性能。
Glass bottles must be thoroughly inspected before they are used for packaging. However, the vision inspection of bottle bottoms for defects remains a challenging task in quality control due to inaccurate localization, the difficulty in detecting defects in the texture region, and the intrinsically nonuniform brightness across the central panel. To overcome these problems, we propose a surface defect detection framework, which is composed of three main parts. First, a new localization method named entropy rate superpixel circle detection (ERSCD), which combines least-squares circle detection and entropy rate superpixel (ERS) with an improved randomized circle detection, is proposed to accurately obtain the region of interest (ROI) of the bottle bottom. Then, according to the structure-property, the ROI is divided into two measurement regions: central panel region and annular texture region. For the former, a defect detection method named frequency-tuned anisotropic diffusion super-pixel segmentation (FTADSP) that integrates frequency-tuned salient region detection (FT), anisotropic diffusion, and an improved superpixel segmentation is proposed to precisely detect the regions and boundaries of defects. For the latter, a defect detection strategy called wavelet transform multiscale filtering (WTMF) based on a wavelet transform and a multiscale filtering algorithm is proposed to reduce the influence of texture and to improve the robustness to localization error. The proposed framework is tested on four data sets obtained by our designed vision system. The experimental results demonstrate that our framework achieves the best performance compared with many traditional methods.
登录
查看更多内容
DOI：
10.1109/wcica.2006.1713895
发表时间：
2006-10
期刊：
2006 6th World Congress on Intelligent Control and Automation
影响因子：
--
作者：
Huan-jun Liu;Yao-nan Wang;Feng Duan
通讯作者：
Huan-jun Liu;Yao-nan Wang;Feng Duan
DOI：
10.1007/978-3-319-16811-1_34
发表时间：
2014-11
期刊：
--
影响因子：
--
作者：
Gökhan Yildirim;S. Süsstrunk
通讯作者：
Gökhan Yildirim;S. Süsstrunk
DOI：
10.1109/cvpr.2009.5206596
发表时间：
2009-06
期刊：
2009 IEEE Conference on Computer Vision and Pattern Recognition
影响因子：
--
作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
通讯作者：
R. Achanta;S. Hemami;F. Estrada;S. Süsstrunk
影响因子：
12.3
作者：
Heying Wang;Jiawei Zhang;Ying Tian;Haiyong Chen;Hexu Sun;Kun Liu
通讯作者：
Heying Wang;Jiawei Zhang;Ying Tian;Haiyong Chen;Hexu Sun;Kun Liu
DOI：
10.1007/s00170-017-1156-6
发表时间：
2018-02
期刊：
The International Journal of Advanced Manufacturing Technology
影响因子：
--
作者：
Chin-Sheng Chen;Chi-Min Weng;Chien-Chuan Tseng
通讯作者：
Chin-Sheng Chen;Chi-Min Weng;Chien-Chuan Tseng