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一个改进的钢铁缺陷检测模型,使用基于YOLO-MFD的多尺度特征融合
Yuqin Feng1, Lingxiao Jin1, Hao Yang1
1School of computer science and technology, Xinjiang University, 777 Huarui Street, Shuimogou District, Urumqi, 830017, the Xinjiang Uygur Autonomous Region, China.
Scientific reports
|December 26, 2025
概括
本研究介绍了YOLO-MFD,这是一款用于检测钢表面缺陷的轻量级模型. 它提高了对小缺陷的准确性,并减少了模型大小,增强了实时工业应用.
科学领域:
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
- 人工智能的人工智能
背景情况:
- 钢表面缺陷检测面临着小目标和复杂背景的挑战.
- 现有的模型往往具有很大的参数数量,阻碍了部署.
- 精确有效的检测对于工业质量控制至关重要.
研究的目的:
- 开发一种轻量级且准确的钢表面缺陷检测模型.
- 解决检测小缺陷的局限性,减少模型复杂性.
- 在工业环境中实现实时缺陷检测.
主要方法:
- 拟议的YOLO-MFD模型基于YOLO11n.
- 引入了多尺度边缘功能增强 (MEFE) 模块,用于微妙的缺陷检测.
- 实现全球至本地空间注意力双向特征金字塔网络 (GLSA-BiFPN) 以实现高效的特征融合.
- 使用组检测头 (Ghead) 通过分组卷积来减少参数.
主要成果:
- 在NEU-DET数据集上,YOLO-MFD实现了83.8%的mAP,比YOLO11n.提高了5.9%.
- 模型参数减少了27%,计算成本减少了6.3%.
- 在检测准确度和轻量级设计之间取得了有效的平衡.
结论:
- 拟议的YOLO-MFD模型显著提高了钢表面缺陷检测的准确性和效率.
- 该模型的轻量级性质使其适合在工业场景中实时部署.
- 对于复杂的工业质量检查,YOLO-MFD提供了一个实用的解决方案.
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