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

Tianbiao Liang (), Tianyuan Liu (), Junliang Wang (), Jie Zhang () and Pai Zheng ()
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Tianbiao Liang: Donghua University
Tianyuan Liu: The Hong Kong Polytechnic University
Junliang Wang: Donghua University
Jie Zhang: Donghua University
Pai Zheng: The Hong Kong Polytechnic University

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 29, 1363-1384

Abstract: 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.

Keywords: Computer vision; Defect inspection; Deep learning; Causal inference; Explainable artificial intelligence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02297-9

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