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Anomaly Detecting Geometric Transformation Network with Outlier Exposure Defect Inspection of Real Industrial Data

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Abstract

Anomaly detection is one of the innovative deep learning (DL) technologies being actively applied in various industrial fields. Among them, the supervised learning approach for anomaly detection requires a large amount of normal and abnormal data. However, in real industrial situations, obtaining abnormal data can be very expensive and difficult. Therefore, in this paper, we used just normal data in a semi-supervised learning approach. 72 geometric transformations of the input sample were added to train a DL network, and to classify the transformation type using the trained network. The proposed method was applied to detect mechanical part defects on an automobile industry production line. Because it is difficult to obtain enough abnormal data in the same region of interest (ROI), to improve classification performance, a method called outlier exposal is suggested, in which the normal data in another ROI is added as abnormal data for training. Abnormal data creates a uniform distribution in the output of the network so that the class of the prediction does not belong to any kind of normal data class. Abnormal data acts the anomaly detector to be generalized more and to increase the anomaly score for new abnormal data. Experimental results verified the proposed method improved performance compared to the conventional approach.

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Acknowledgements

This work was supported by the Center for Advanced Meta-Materials (CAMM) funded by the Ministry of Science and ICT as Global Frontier Project (CAMM- Nos. 2019M3A6B3031048 and 2014M3A6B3063700) and partly supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. 2021R1A2C1010057).

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Authors and Affiliations

  1. School of Mechanical Engineering, Pusan National University, 2, Busandaehak-Ro 63Beon-Gil, Geumjeong-Gu, Busan, 609-735, South Korea

    Yong-Wan Kwon & Dong-Joong Kang

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  1. Yong-Wan Kwon
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  2. Dong-Joong Kang
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Both authors read and approved the final manuscript.

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Correspondence to Dong-Joong Kang.

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Kwon, YW., Kang, DJ. Anomaly Detecting Geometric Transformation Network with Outlier Exposure Defect Inspection of Real Industrial Data. Int. J. Precis. Eng. Manuf. 24, 73–81 (2023). https://doi.org/10.1007/s12541-022-00736-w

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