合并-YOLO:在智能物流场景中准确检测书包装缺陷的模型
Zhaohua Wang1,2, Yirong Luo1,2, Yanping Du1,2
1School of Mechanical and Electrical Engineering, Beijing Institute of Graphic Communication, Beijing, People's Republic of China.
PloS one
|January 8, 2026
概括
本研究介绍了用于加强书籍包装质量控制的 Merge-YOLO 模型. 该模型有效地检测低对比度,小型和不规则的缺陷,提高物流效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 物流管理物流管理
背景情况:
- 由于数字化,图书物流规模正在扩大.
- 目前的质量检查使用通用模型,忽视了缺陷的具体情况.
- 需要专门的模型来解决独特的书包装缺陷.
研究的目的:
- 提出合并-YOLO模型用于书籍包装缺陷检测.
- 解决低对比度,小尺寸和不规则形状的挑战.
- 提高质量控制的准确性和效率.
主要方法:
- 开发了WT-C3k2模块,用于使用波形变换和多层卷积进行增强的特征提取.
- 推出了具有适应四边形窗口的QA变压器,用于不规则的缺陷检测.
- 实现了DySample动态升级器,以有效地保存细节.
主要成果:
- 在书包装缺陷数据集上,获得了95.8%的精度,93.6%的回忆率和94.1%的mAP@0.5.
- 在所有指标中表现优于基线YOLOv11和传统算法.
- 证明了高效和准确的检测能力.
结论:
- 合并-YOLO模型为书包装的自动化质量控制提供了显著的进步.
- 拟议的模型有效地处理复杂的缺陷特征.
- 为书籍供应链质量保证提供有效的解决方案.
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