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A novel class-level weighted partial domain adaptation network for defect detection

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Abstract

Recently, unsupervised domain adaptation methods have been increasingly applied to address the domain shift problems in defect detection. However, the effectiveness of most existing methods is based on the identical category space in the source and target domains. A more practical scenario is when the target domain only contains a subset of source categories, i.e., partial domain adaptation, which has not been well-resolved. To this end, a novel class-level weighted partial domain adaptation network (CWPDAN) is proposed for defect detection. Specifically, a hybrid weighting mechanism is derived from a defect classifier and an auxiliary domain classifier. In this case, the weighting mechanism is injected into both the defect classifier and the fine-grained domain adaptation strategy. As such, the shared category space across domains can be aligned well and the outlier categories can be identified and filtered out to alleviate negative transfer. Comprehensive partial domain adaptation experiments verify that the proposed CWPDAN can achieve 95.07% and 98.27% average accuracy on a tire defect dataset and a benchmark dataset, respectively, outperforming other state-of-the-art methods.

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Data Availability

The data that support the findings of this study are available from Zhongce Rubber Group but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62293504, 62293500 in part by the National Key Research and Development Plan of China under Grant 2022YFC2403103, in part by the Major Scientific Project of Zhejiang Laboratory under Grant 2020MC0AE01, and in part by the Zhejiang University Robotics Institute (Yuyao) Project under Grant K12001

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Authors and Affiliations

  1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, Zhejiang, China

    Yulong Zhang, Yilin Wang, Li Zheng, Jinshui Chen & Jiangang Lu

  2. Zhongce Rubber Group Co., Ltd, Hangzhou, 310018, Zhejiang, China

    Zhiqiang Jiang & Li Zheng

  3. Research Center for Intelligent Chips and Devices, Zhejiang Laboratory, Hangzhou, 311100, Zhejiang, China

    Jiangang Lu

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  1. Yulong Zhang
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  2. Yilin Wang
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  3. Zhiqiang Jiang
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  4. Li Zheng
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  5. Jinshui Chen
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  6. Jiangang Lu
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Correspondence to Jiangang Lu.

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Zhang, Y., Wang, Y., Jiang, Z. et al. A novel class-level weighted partial domain adaptation network for defect detection. Appl Intell 53, 23083–23096 (2023). https://doi.org/10.1007/s10489-023-04733-y

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