Abstract
It is the main task of visual anomaly detection to find local regions whose saliency is inconsistent with normal appearance. However, existing mainstream anomaly detection models suffer from low detection accuracy and poor generalization performance. Therefore, this paper designs an unsupervised surface anomaly detection model based on attention and atrous spatial pyramid pooling. The proposed model learns anomaly images and their normal reconstruction and simultaneously learns the decision boundaries of normal and anomaly images. The method utilizes a squeeze-and-excitation block to assign attention to feature channels to improve the sensitivity of related favorable features, thus enhancing the model’s ability to learn normal and anomaly boundaries. In addition, atrous spatial pyramid pooling is introduced in the discriminative sub-network to obtain the multi-scale semantic information of the training image, which improves the detection ability of defects of different sizes and enhances the universality of the model. The superiority of our method is demonstrated in the anomaly detection benchmark MVTec dataset.
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Acknowledgments
This paper is supported by the National Natural Science Foundation of China, under Grant No. 62162026, the Science and Technology Key Research and Development Program of Jiangxi Province, under Grant No. 20202BBEL53004 and Science and Technology Project supported by Education Department of Jiangxi Province, under Grant No. GJJ210611.
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Huang, Y., Xie, X., Ning, W., Wu, D., Li, Z., Yang, H. (2023). An Unsupervised Surface Anomaly Detection Method Based on Attention and ASPP. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_16
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