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Detection of small defects on packaging prints under sample-few conditions

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Abstract

The objective of this paper is to address the challenges associated with acquiring defect samples and detecting small defects in print defect detection. We propose a methodology for generating defect samples using the pix2pix-HDNet network, which first generates coarse defects in designated areas of defect-free samples and then adjusts their brightness for visual consistency with the background, resulting in realistic defect samples. Additionally, we improve YOLOv7 and propose a print defect detection algorithm called DF-YOLOv7, which comprises two innovative modules: the Detail Extraction Module (DEM) and the Feature Enhancement Module with Omni-dimensional Dynamic Convolution (FEM-ODConv). DEM enhances the network’s ability to capture detailed information and refine the edge information of defects, while FEM-ODConv improves the extraction of contextual and semantic information, enhancing the weak features of small defects and effectively suppressing background interference. We collected three common print defects, namely ink blot, black point, and missing print, and expanded the defect samples using pix2pix-HDNet. Training DF-YOLOv7 with the expanded dataset achieved an accuracy of 92.1%, surpassing several benchmark models and state-of-the-art methods. Our results demonstrate that the proposed pix2pix-HDNet effectively generates realistic defect samples, facilitating the training of a defect detection network, and that DF-YOLOv7 provides more accurate detection of small print defects.

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Data availability

Some or all data, models, or code generated or used during the study are available from the corresponding author by request.

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Funding

This work was supported by the NSFC via Project 62076200, Natural Science Foundation of Shaanxi Province No.2021JM-340, Science Technology Project of Weinan 2021ZDYF-GYCX-150.

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Authors and Affiliations

  1. College of Faculty of Printing, Packaging Engineering and Digital Media Technology, Xi’an University of Technology, Xi’an, 710048, China

    Haiwen Liu, Yuanlin Zheng, Kaiyang Liao, Fuqiang Yang & Bangyong Sun

  2. Weinan Daily Printing Factory, Weinan City, 714000, Shaanxi Province, China

    Chongjun Zhong

Authors
  1. Haiwen Liu
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  2. Yuanlin Zheng
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  3. Kaiyang Liao
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  4. Fuqiang Yang
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  5. Bangyong Sun
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Contributions

Liu. obtained the experimental data and wrote the main manuscript text, Zheng. and Liao. guided the experimental model improvement and experimental data sorting, and Yang. prepared Figs. 1, 2, 3, 4 and 5. All authors reviewed the manuscript. Sun. and Zhong. supervised the writing of the thesis.

Corresponding authors

Correspondence to Haiwen Liu or Yuanlin Zheng.

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Liu, H., Zheng, Y., Liao, K. et al. Detection of small defects on packaging prints under sample-few conditions. SIViP 19, 323 (2025). https://doi.org/10.1007/s11760-025-03882-6

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  • DOI: https://doi.org/10.1007/s11760-025-03882-6

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