基于YOLOv7的管道接的表面缺陷检测算法研究
1School of Material Science and Engineering, Xi'an Shiyou University, No. 18, East Section of Electronic Second Road, Xi'an, 710065, Shaanxi, China. xqxu@xsyu.edu.cn.
Scientific reports
|January 22, 2024
概括
这项研究引入了一种改进的YOLOv7模型用于管道接缺陷检测,显著减少错过的检测并提高准确性. 改进的模型实现了78.6%的mAP,超过了以前的方法.
科学领域:
- 机械工程 机械工程
- 计算机视觉 计算机视觉
- 材料科学 材料科学 材料科学
背景情况:
- 传统的接表面缺陷检测方法的准确性低,泄漏率高.
- 现有的深度学习模型在特征提取和干扰接缺陷图像方面遇到了困难.
研究的目的:
- 为了提高管道接表面缺陷检测的准确性和减少泄漏率.
- 改进接缺陷识别中的特征提取能力和目标表示.
主要方法:
- 一个改进的YOLOv7模型,包含一个Le-HorBlock模块,用于二次空间交互.
- 集成坐标注意 (CoordAtt) 块以增强特征表示和抑制干扰.
- 用SIoU损失取代CIoU损失,以实现最佳的融合和减少模型自由度.
- 利用了2000个管道接缺陷图像的新的大规模数据集.
主要成果:
- 改进的YOLOv7模型显示,与原始网络相比,错过检测率显著降低.
- 实现了平均平均精度 (mAP@80.5) 78.6%,比原始YOLOv7模型提高了15.9%.
- 增强型号的性能优于原始YOLOv7和其他经典目标检测网络.
结论:
- 建议改进的YOLOv7模型有效地提高了管道接表面缺陷检测的准确性.
- 整合Le-HorBlock,CoordAtt和SIoU损失有助于卓越的特征提取和检测性能.
- 这种先进的模型为在工业应用中识别接缺陷提供了更强大的解决方案.
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