关键词: 分布鲁棒优化, 标签平滑, 工业图像异常识别

Abstract: Surface anomaly can lead to defects in the appearance, quality, and performance of industrial products, thereby reducing production efficiency and increasing safety risks, ultimately resulting in economic and reputational losses for manufacturing enterprises. Therefore, accurate identification and detection of surface anomalies in industrial products is of paramount importance. With the rapid development of artificial intelligence, computer vision techniques based on deep neural networks (DNNs) have emerged quickly and are extensively used in surface anomaly detection for industrial products. However, due to the scarcity of surface anomalies, uncertainty in defect types, and high cost of manual labeling, the recognition accuracy of DNNs remains suboptimal. To address this issue, this paper proposes a distributionally robust optimization generative (DRO-G) model with label smoothing (LS). This model operates in two phases. In the first phase, the regularization effect of LS is extended and demonstrated to be able to generate new images. In the second phase, the generated images are used to train DNNs for anomaly detection. Moreover, this paper constructs a label smoothing-stochastic gradient (LS-SG) algorithm to approximately solve the model. In the first phase, gradient ascent is used to incorporate the LS regularization effect into existing images and generate new ones. In the second phase, gradient descent is employed to train DNNs to identify anomalous images. Furthermore, this paper conducts simulation experiments using multiple types of surface anomaly data from four products categories: grid, carpet, wood, and screw, in the MVTecAD dataset. The results demonstrate that the proposed algorithm can effectively expand the surface anomaly dataset and improve the recognition accuracy of DNNs for certain product anomalies, while also exhibiting a degree of noise resistance. This approach not only assists enterprises in improving product quality and production efficiency but also offers an innovative solution for anomaly recognition and detection in industrial imaging. This approach not only aids enterprises in enhancing product quality and production efficiency but also provides an innovative solution for anomaly recognition and detection in industrial imaging.

Key words: distributionally robust optimization, label smoothing (LS), industrial image anomaly recognition