Deep-learning-based anomaly detection for lace defect inspection employing videos in production line
Deep-learning-based anomaly detection for lace defect inspection employing videos in production line
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DOI：
10.1016/j.aei.2021.101471
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发表时间：
2021-11-22
影响因子：
8.8
通讯作者：
Huang, Biqing
中科院分区：
文献类型：
--
作者：
Lu, Bingyu;Xu, Ding;Huang, Biqing
缺陷检验对于保证工业产品的质量起着至关重要的作用。目前应用最广泛的人工视觉检测方法存在成本高、效率低等缺点，这给自动缺陷检测算法在实际生产中的应用带来了迫切的需求。然而，由于实际生产环境中收集的数据与现成的数据集之间存在差距，很少有工业生产线使用自动检测设备。花边是完全依靠人工进行缺陷检验的工业产品之一。花边复杂而精细的纹理使得现有的基于图像的缺陷检测方法难以提取规则图案。在本文中，我们建议收集编织阶段的蕾丝视频，并设计一个基于深度学习的异常检测框架来检测蕾丝缺陷。该框架包含三个阶段，即视频预处理阶段、像素重建阶段和像素分类阶段。在离线阶段，只需要无缺陷的花边视频来训练像素重建模型并通过我们的自适应阈值方法计算检测阈值。在在线阶段，所提出的框架重建花边视频并使用重建误差和预设阈值执行缺陷检查。据我们所知，这篇论文是第一个通过视频检测织物缺陷的论文。人工缺陷视频的实验结果证明了所提出框架的有效性。
Defect inspection plays an essential role in ensuring quality of industrial products. The most widely used human visual inspection method has some drawbacks such as high cost and low efficiency, which bring an eager demand for the application of automatic defect inspection algorithm in actual production. However, few industrial production lines use automatic detection devices due to the gap between data collected in the actual production environment and ready-made datasets. Lace is one of the industrial products which completely depends on manual defect inspection. The complex and fine texture of lace makes it difficult to extract regular patterns using the existing image-based defect inspection methods. In this paper, we propose to collect lace videos in the weaving stage and design a deep-learning-based anomaly detection framework to detect lace defects. The framework contains three stages, namely video pre-processing stage, pixel reconstruction stage and pixel classification stage. In the offline phase, only defect-free lace videos are needed to train the pixel reconstruction model and calculate the detection threshold by our adaptive thresholding method. In the online phase, the proposed framework reconstructs lace videos and performs defect inspection using reconstruction error and the pre-set threshold. As far as we know, this paper the first to detect fabric defects by videos. Experimental results on artificial defect videos demonstrate the effectiveness of the proposed framework.