Mei Shuang, Wang Yudan, Wen Guojun
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China.
Sensors (Basel). 2018 Apr 2;18(4):1064. doi: 10.3390/s18041064.
Fabric defect detection is a necessary and essential step of quality control in the textile manufacturing industry. Traditional fabric inspections are usually performed by manual visual methods, which are low in efficiency and poor in precision for long-term industrial applications. In this paper, we propose an unsupervised learning-based automated approach to detect and localize fabric defects without any manual intervention. This approach is used to reconstruct image patches with a convolutional denoising autoencoder network at multiple Gaussian pyramid levels and to synthesize detection results from the corresponding resolution channels. The reconstruction residual of each image patch is used as the indicator for direct pixel-wise prediction. By segmenting and synthesizing the reconstruction residual map at each resolution level, the final inspection result can be generated. This newly developed method has several prominent advantages for fabric defect detection. First, it can be trained with only a small amount of defect-free samples. This is especially important for situations in which collecting large amounts of defective samples is difficult and impracticable. Second, owing to the multi-modal integration strategy, it is relatively more robust and accurate compared to general inspection methods (the results at each resolution level can be viewed as a modality). Third, according to our results, it can address multiple types of textile fabrics, from simple to more complex. Experimental results demonstrate that the proposed model is robust and yields good overall performance with high precision and acceptable recall rates.
织物缺陷检测是纺织制造业质量控制中必要且重要的一步。传统的织物检查通常采用人工视觉方法，对于长期工业应用而言，效率较低且精度较差。在本文中，我们提出了一种基于无监督学习的自动化方法，无需任何人工干预即可检测和定位织物缺陷。该方法用于在多个高斯金字塔级别使用卷积去噪自动编码器网络重建图像块，并从相应分辨率通道合成检测结果。每个图像块的重建残差用作直接逐像素预测的指标。通过在每个分辨率级别分割和合成重建残差图，可以生成最终的检查结果。这种新开发的方法在织物缺陷检测方面具有几个突出的优点。首先，它仅需使用少量无缺陷样本进行训练。这对于难以收集大量有缺陷样本的情况尤为重要。其次，由于采用了多模态集成策略，与一般检查方法相比，它相对更稳健、更准确（每个分辨率级别的结果可视为一种模态）。第三，根据我们的结果，它可以处理多种类型的纺织面料，从简单到复杂。实验结果表明，所提出的模型稳健，具有高精度和可接受的召回率，整体性能良好。
