Abstract
In this paper, our goal is to detect unknown defects in high-resolution images in the absence of anomalous data. Anomaly detection is usually performed at image-level or pixel-level. Considering that pixel-level anomaly classification achieves better representation learning in a finer-grained manner, we regard data augmentation transforms as a self-supervised segmentation task from which to extract the critical and representative information from images. Due to the unpredictability of anomalies in real scenarios, we propose a novel abnormal sample simulation strategy which augmented patches are randomly pasted to original image to create a generalized anomalous pattern. Following the framework of self-supervised, segmenting augmented patches is used as a proxy task in the training phase to extract representation separating normal and abnormal patterns, thus constructing a one-class classifier with a robust decision boundary. During the inference phase, the classifier is used to perform anomaly detection on the test data, while directly determining regions of unknown defects in an end-to-end manner. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed SSAPS outperforms any other self-supervised based methods in anomaly detection. Code is available at https://github.com/BadSeedX/SSAPS.
Supported by Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province, Central South University.
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The paper is supported by the Open Fund of Science and Technology on Parallel and Distributed Processing Laboratory under Grant WDZC20215250116.
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Long, J., Yang, Y., Hua, L., Ou, Y. (2023). Self-supervised Augmented Patches Segmentation for Anomaly Detection. In: Wang, L., Gall, J., Chin, TJ., Sato, I., Chellappa, R. (eds) Computer Vision – ACCV 2022. ACCV 2022. Lecture Notes in Computer Science, vol 13842. Springer, Cham. https://doi.org/10.1007/978-3-031-26284-5_6
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