MFDH-Net:用于多级特征融合和交叉传感解头的缺陷检测网络
Laomo Zhang1, Zeyu Yang2, Ying Ma3
1School of Software, Henan University of Engineering, Zhengzhou, 451191, China. zlm@haue.edu.cn.
Scientific reports
|February 18, 2026
概括
通过整合全球和本地特征,多层次聚合和空间语义融合,MFDH-Net增强了工业缺陷检测. 这个创新的网络实现了最先进的性能,改善了智能制造中的质量控制.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 制造业 制造技术 制造技术
背景情况:
- 工业缺陷检测对于智能制造中的质量管理至关重要.
- 手动检查效率低下,容易出现错误,而自动化系统面临诸如类间相似性和多层次缺陷等挑战.
研究的目的:
- 推出MFDH-Net,这是一个创新的网络,用于强大的工业缺陷检测.
- 应对高相似度,弱语义信息和不同尺度的缺陷检测方面的挑战.
主要方法:
- 开发了一种双域特征提取网络 (DFE-Net),用于双向的全球和本地缺陷信息捕获.
- 设计了一个多级特征聚合网络 (MFA-Net),具有全级融合和适应权重,用于深度特征交互.
- 整合了空间语义融合模块 (SSFM) 进行像素级对齐,以及交叉意识解头 (CDH) 与交叉意识注意模块 (CAM) 进行细粒度识别,特别是对于小缺陷.
主要成果:
- 获得了高平均平均精度 (mAP@.5) 分数:钢材94.3%,PCB96.6%,GC10-Net71.7%,汽车表面缺陷数据集98.9%.
- 达到每秒52 (FPS) 的处理速度.
- 证明了最先进的性能 (SOTA),表现优于现有方法.
结论:
- MFDH-Net有效地克服了工业缺陷检测方面的挑战,包括复杂的背景和小目标.
- 拟议的网络为智能制造中的质量控制提供了可靠的技术保证.
- 结果强调了网络在各种工业应用中的效率和准确性.
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