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Self-Supervised Anomaly Detection Algorithm for Industrial Components Based on Multi-scale Feature Fusion
Artificial Intelligence | 更新时间：2024-12-30
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    • Self-Supervised Anomaly Detection Algorithm for Industrial Components Based on Multi-scale Feature Fusion

    • “In the field of industrial component anomaly detection, researchers have proposed a self supervised algorithm based on multi-scale feature fusion, which effectively improves detection accuracy and provides a new solution for industrial production quality control.”
    • Software Guide   Vol. 23, Issue 12, Pages: 44-52(2024)

    • DOI：10.11907/rjdk.232186    

      CLC： TP391.41

    • Published：16 December 2024，

      Received：21 November 2023，

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  • LI Qian,GAO Lin,LI Siyuan,et al.Self-Supervised Anomaly Detection Algorithm for Industrial Components Based on Multi-scale Feature Fusion[J].Software Guide,2024,23(12):44-52. DOI： 10.11907/rjdk.232186.

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