Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 发表日期：

    2025年2月21日

查看全文
  • Haiwen Liu,
  • Yuanlin Zheng,
  • Kaiyang Liao,
  • Fuqiang Yang,
  • Bangyong Sun,
  • Chongjun Zhong

摘要

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.