Print Defect Recognition Methods Based on Improved Convolutional Neural Networks

LI Hongfeng; TIAN Mingyu; ZHANG Yanjun; GAO Zhenqing

doi:10.19554/j.cnki.1001-3563.2026.13.025

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Packaging Engineering ›› 2026, Vol. 47 ›› Issue (13) : 223-232. DOI: 10.19554/j.cnki.1001-3563.2026.13.025

Automatic and Intelligent Technology

Print Defect Recognition Methods Based on Improved Convolutional Neural Networks

LI Hongfeng^*, TIAN Mingyu, ZHANG Yanjun, GAO Zhenqing

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Abstract

The work aims to propose a print-defect recognition method based on an improved convolutional neural network (CNN) for limited-sample scenarios to improve the precision and efficiency of print quality inspection, and alleviate the problems of complex manual feature engineering and insufficient adaptability to diverse defect morphologies in manual inspection and conventional machine-vision methods by improving the automatic feature extraction ability and limited-sample generalization ability of the CNN model. An improved CNN model was designed for fine-grained feature extraction and small-scale defect recognition in printed-product images. By optimizing the network structure and incorporating multiple image processing techniques, the model was enhanced to improve its robustness and generalization capability under complex printing conditions. Experimental results showed that the improved CNN model achieved an accuracy of 98.8%, a precision of 98.5%, a recall of 97.1%, and an F1-score of 97.8% in the print-defect recognition task. Compared with the ResNet-18 model, which achieved an accuracy of 92.5%, the proposed model improved the accuracy by 6.3 percentage points, demonstrating better recognition performance for small-scale defects and color anomalies. In conclusion, the proposed improved CNN model significantly enhances the performance of print quality inspection, provides an effective technical approach for applying deep learning to industrial quality inspection, and demonstrates great potential in practical applications.

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deep learning / convolutional neural networks / print defect recognition / small sample

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LI Hongfeng, TIAN Mingyu, ZHANG Yanjun, GAO Zhenqing. Print Defect Recognition Methods Based on Improved Convolutional Neural Networks[J]. Packaging Engineering. 2026, 47(13): 223-232 https://doi.org/10.19554/j.cnki.1001-3563.2026.13.025

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