SIAD: Self-supervised Image Anomaly Detection System

• URL: http://arxiv.org/abs/2208.04173v2
• Date: Sun, 8 Oct 2023 15:55:35 GMT
• Title: SIAD: Self-supervised Image Anomaly Detection System
• Authors: Jiawei Li, Chenxi Lan, Xinyi Zhang, Bolin Jiang, Yuqiu Xie, Naiqi Li, Yan Liu, Yaowei Li, Enze Huo, Bin Chen
• Abstract summary: This paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms.
• Score: 18.410995759781006
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent trends in AIGC effectively boosted the application of visual inspection. However, most of the available systems work in a human-in-the-loop manner and can not provide long-term support to the online application. To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios. Benefit from the self-supervised learning, SsaA is effective to establish a visual inspection application for the whole life-cycle of manufacturing. In the early stage, with only the anomaly-free data, the unsupervised algorithms are adopted to process the pretext task and generate coarse labels for the following data. Then supervised algorithms are trained for the downstream task. With user-friendly web-based interfaces, SsaA is very convenient to integrate and deploy both of the unsupervised and supervised algorithms. So far, the SsaA system has been adopted for some real-life industrial applications.

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