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

Tianbiao Liang, Tianyuan Liu, Junliang Wang, Jie Zhang, Pai Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

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.

Original languageEnglish
Pages (from-to)1-22
Number of pages22
JournalJournal of Intelligent Manufacturing
DOIs
Publication statusPublished - 22 Jan 2024

Keywords

  • Causal inference
  • Computer vision
  • Deep learning
  • Defect inspection
  • Explainable artificial intelligence

ASJC Scopus subject areas

  • Software
  • Industrial and Manufacturing Engineering
  • Artificial Intelligence

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