论文标题
用于表面缺陷检查的轻质重建网络
A Lightweight Reconstruction Network for Surface Defect Inspection
论文作者
论文摘要
当前，大多数深度学习方法无法解决工业产品缺陷样本稀缺的问题和特征的显着差异。本文提出了基于重建网络的无监督缺陷检测算法，该算法仅使用大量易于获得的缺陷样品数据来实现。该网络包括两个部分：图像重建和表面缺陷区域检测。重建网络是通过具有轻量级结构的完全卷积自动编码器设计的。仅使用少量的普通样本进行训练，因此重建网络可以成为无缺陷的重建图像。结合结构损失和$ \ mathit {l} 1 $损失的函数被提出为重建网络的损失函数，以解决不规则纹理表面缺陷的不良检测问题。此外，重建图像和要测试的图像的残差用作缺陷的可能区域，常规图像操作可以实现故障的位置。所提出的重建网络的无监督缺陷检测算法用于多个缺陷图像样本集。与其他类似的算法相比，结果表明，重建网络的无监督缺陷检测算法具有强大的稳健性和准确性。
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and $\mathit{L}1$ loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.
