基于双脊柱网络检测钢的表面缺陷:MBDNet-注意力-YOLO
Xinyu Wang1, Shuhui Ma2, Shiting Wu1
1School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了MBY (MBDNet-Attention-YOLO),这是一个用于自动检测钢表面缺陷的新型框架. MBY实现了高精度和实时性能,解决了现有方法对各种缺陷类型和复杂表面的局限性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 制造业 工程 制造工程
背景情况:
- 钢铁制造业的自动表面缺陷检测对于质量控制至关重要,但受到各种缺陷形态和复杂背景的挑战.
- 现有的方法,包括经典视觉和深度学习,在准确性,稳定性,规模变化和实时处理上受到限制的硬件上扎.
研究的目的:
- 开发一个轻量级和准确的框架,用于自动化钢表面缺陷检测,克服当前方法的局限性.
- 为了提高亚毫米缺陷的检测精度,增强对纹理背景和尺寸变化的缺陷的稳定性,并实现实时吞吐量.
主要方法:
- 推出MBY (MBDNet-Attention-YOLO),一个框架,将一个新的MBDNet骨干与YOLO检测头结合起来.
- MBDNet骨干具有HGStem用于丰富的表示,动态对齐融合 (DAF)用于自适应的跨尺度特征融合,以及C2f-DWR用于扩展的受体场.
- 一个MultiSEAM模块增强了特征表示,内部SIoU损失提高了界限框本地化准确性.
主要成果:
- 在NEU-DET基准指标上,MBY实现了85.8%的mAP@0.5,在PVEL-AD基准指标上达到75.9%的mAP@0.5,超过了现有的最先进方法.
- 该模型在NVIDIA Jetson Xavier上展示了实时推断功能,适合资源有限的工业环境.
- 废弃性研究证实了单个组件的有效性和MBY在不同缺陷尺度和表面条件的整体稳定性.
结论:
- MBY在自动化钢表面缺陷检测方面取得了重大进展,平衡了高精度,效率和可部署性.
- 拟议的框架为下一代工业质量控制系统提供了务实的解决方案,能够处理复杂的缺陷场景.
- MBY的新组件和架构有助于卓越的性能和稳定性,使其成为钢铁制造业的宝贵工具.
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