Journal of Shandong University(Engineering Science) ›› 2025, Vol. 55 ›› Issue (4): 40-47.doi: 10.6040/j.issn.1672-3961.0.2024.183

• Special Issue for Deep Learning with Vision • Previous Articles     Next Articles

Industrial product surface defect detection based on self supervised convolution and parameter free attention mechanism

ZHOU Qunying, SUI Jiacheng, ZHANG Ji*, WANG Hongyuan   

  1. ZHOU Qunying, SUI Jiacheng, ZHANG Ji*, WANG Hongyuan (College of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, Jiangsu, China
  • Published:2025-08-31

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Abstract: Currently, surface defect detection in industrial products using deep learning faced several challenges. These included a limited number of negative sample datasets, susceptibility to complex industrial environments that hindered effective feature extraction, and high labor cost for labeling datasets. In order to solve the above problems,a knowledge distillation-based surface defect detection method for industrial products was proposed. The model incorporated a self-supervised predictive convolution module and a parameter-free attention mechanism. It transferred the rich feature knowledge learned by the teacher network to the student network. This approach effectively improved the model feature expression and localized defects at the pixel level. The model was experimented on the MVTec-AD dataset, and the comparison results with the state-of-the-art experimental methods showed that its detection and localization metrics were improved on AROC. The results proved that the method could improve the model's detection and localization capabilities.

Key words: surface defect detection of industrial products, deep learning, knowledge distillation, self supervised prediction, attention mechanism

CLC Number: 

  • TP391
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