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Causal deep learning for explainable vision-based quality inspection under visual interference

Author

Listed:
  • Tianbiao Liang

    (Donghua University
    Chongqing University)

  • Tianyuan Liu

    (The Hong Kong Polytechnic University)

  • Junliang Wang

    (Donghua University)

  • Jie Zhang

    (Donghua University)

  • Pai Zheng

    (The Hong Kong Polytechnic University)

Abstract

Vision-based quality inspection is a key step to ensure the quality control of complex industrial products. However, accurate defect recognition for complex products with information-rich, structure-irregular and significantly different patterns is still a tough problem, since it causes the strong visual interference. This paper proposes a causal deep learning method (CDLM) to tackle the explainable vision-based quality inspection under visual interference. First, a structural causal model for defect recognition of complex industrial products is constructed and a causal intervention strategy to overcome the background interference is generated. Second, a defect-guided recognition neural network (DGRNN) is constructed, which can realize accurate defect recognition under the training of CDLM via feature-wise causal intervention using two sub-networks with feature difference mechanism. Finally, the causality between defect features and defective product labels can guide the DGRNN to complete the accurate and explainable learning of defect in a causal direction of optimization. Quantitative experiments show that the proposed method achieves recognition accuracy of 94.09% and 93.95% on two fabric datasets respectively, which outperforms the cutting-edge inspection models. Besides, Grad-CAM visualization experiments show that the proposed method successfully captures the data causality and realizes the explainable defect recognition.

Suggested Citation

  • Tianbiao Liang & Tianyuan Liu & Junliang Wang & Jie Zhang & Pai Zheng, 2025. "Causal deep learning for explainable vision-based quality inspection under visual interference," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 1363-1384, February.
  • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02297-9
    DOI: 10.1007/s10845-023-02297-9
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