Method of Industrial Product Surface Defect Detection Based on Dual-channel Residual Networks

HU Yichuan; CHEN Xingwang; WANG Shu; XIAN Yingmei; ZHAO Chuanmin; LUO Gan

doi:10.19554/j.cnki.1001-3563.2025.13.025

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Packaging Engineering ›› 2025, Vol. 46 ›› Issue (13) : 220-232. DOI: 10.19554/j.cnki.1001-3563.2025.13.025

Automatic and Intelligent Technology

Method of Industrial Product Surface Defect Detection Based on Dual-channel Residual Networks

HU Yichuan, CHEN Xingwang, WANG Shu^*, XIAN Yingmei, ZHAO Chuanmin, LUO Gan

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Abstract

With the rapid development of computer vision technology, defect detection has become increasingly important in industrial manufacturing, quality control, and especially in cigarette production. The work aims to propose a dual-channel residual surface defect detection method to address the issues of low detection efficiency, poor generalization, and susceptibility to subjective factors in traditional morphological defect detection methods. Firstly, based on the similarity in color and texture distribution between downsampled images of positive samples and downsampled and interpolated images of defect samples, an image preprocessing module was designed to effectively enhance the contrast difference in defect regions. Secondly, a dual-channel residual network structure was designed, utilizing residual connections to enhance the model's feature extraction capability for defect regions, while incorporating skip connections to mitigate the issue of gradient vanishing in deep features. Finally, the LBP algorithm was employed to calculate pixel distance differences between the prediction results of the two models, and defect localization was achieved through coarse-grained screening and fine-grained verification. Experimental results across various scenarios demonstrated that the proposed method achieved a pixel-level detection accuracy of 98%, showcasing high detection accuracy and generalization capability, as well as superior performance in real-world production lines. The proposed method effectively reduces the reliance on non-defect regions during feature extraction, strengthens the extraction of high-frequency information in defect regions, and further improves the efficiency of defect detection in actual production lines.

Key words

prior image / semantic similarity / twin network / residual network / defect detection

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HU Yichuan, CHEN Xingwang, WANG Shu, XIAN Yingmei, ZHAO Chuanmin, LUO Gan. Method of Industrial Product Surface Defect Detection Based on Dual-channel Residual Networks[J]. Packaging Engineering. 2025, 46(13): 220-232 https://doi.org/10.19554/j.cnki.1001-3563.2025.13.025

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