DAssd-Net:基于多分支扩展卷积聚合和多域感知检测头的轻质钢表面缺陷检测模型
Ji Wang1, Peiquan Xu1,2, Leijun Li3
1School of Materials Science and Engineering, Shanghai University of Engineering Science, Shanghai 201620, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
一个新的轻量级模型,DAssd-Net,使用多分支扩展卷曲精确检测钢表面缺陷. 与YOLOv8.8相比,它在显著较小的模型大小下实现了高精度.
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
- 人工智能的人工智能
背景情况:
- 钢铁生产容易出现表面缺陷,如裂和毛孔.
- 准确及时检测缺陷对于保持钢材质量和性能至关重要.
研究的目的:
- 开发一个轻量级和高效的模型,用于钢表面缺陷检测.
- 提高缺陷检测系统的准确性和降低计算复杂性.
主要方法:
- 拟议的DAssd-Net模型具有多分支扩展卷积聚合.
- 集成的扩展卷积和通道/空间注意力融合模块 (DCM/DSM),用于增强特征提取.
- 利用热图可视化用于模型分析和感应场优化.
主要成果:
- 在NEU-DET数据集上,DAssd-Net实现了81.97%的平均精度 (mAP).
- 该模型大小仅为18.7 MB,比现有模型小得多.
- 在mAP中表现比YOLOv8优于4.69%,同时重量更轻.
结论:
- DAssd-Net为钢表面缺陷检测提供了一个高效和轻量级的解决方案.
- 该模型的架构增强了空间意识,并抑制了多余的通道特征.
- 展示了实时工业缺陷监测的有希望的方法.
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