改进了基于YOLOv7的钢表面缺陷检测算法
Yinghong Xie1, Biao Yin1, Xiaowei Han2
1School of Information Engineering, Shenyang University, Shenyang 110003, China.
Mathematical biosciences and engineering : MBE
|February 2, 2024
概括
这项研究增强了YOLOv7算法用于钢表面缺陷检测,改善了小目标识别. 这种新的方法在基准数据集上提高了高达6%的平均精度 (mAP).
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:
- 钢表面缺陷检测对于质量控制至关重要.
- 现有的YOLOv7算法在检测小目标和概括方面存在局限性.
- 精确检测表面缺陷对于工业应用至关重要.
研究的目的:
- 为了提高YOLOv7算法对钢表面缺陷的检测能力和模型概括性.
- 为了提高小缺陷检测的性能,使用改进的YOLOv7模型.
- 解决目前用于识别钢表面微妙缺陷的算法的局限性.
主要方法:
- 设计了一个变压器-InceptionDWConvolution (TI) 模块来增强小物体检测.
- 引入了空间金字塔聚合快速跨阶段部分通道 (SPPFCSPC) 以改善培训.
- 整合了一个全球关注机制 (GAM),以关注相关的缺陷特征.
- 利用Mish激活功能来更好地概括和提取特征.
- 开发了一个最小部分距离交叉点与联盟 (MPDIoU) 损失函数,以实现准确的定位.
主要成果:
- 改进的YOLOv7模型在NEU-DET数据集上实现了6%的平均平均精度 (mAP) 增加.
- 在VOC2012数据集中观察到2.6%的mAP改善.
- 拟议的算法在检测小钢表面缺陷方面表现得更好.
结论:
- 增强的YOLOv7算法有效地改善了钢面上的小缺陷检测.
- TI模块,SPPFCSPC,GAM,Mish功能和MPDIoU的集成有助于提高性能.
- 开发的模型显示出在钢铁质量检查中工业应用的巨大潜力.
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