注意引导的YOLOv5s-SDF模型用于准确检测钢带表面缺陷
Kun Liu1, Xinbo Chang2, Hongru Ma1
1Qingdao Campus,PLA Naval Aviation University, Qingdao, 266000, China.
Scientific reports
|November 26, 2025
概括
本研究介绍了YOLOv5s-SDF算法,用于改进带钢表面缺陷检测. 改进后的模型显示出更高的精度和回忆力,有助于质量控制.
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:
- 表面缺陷在钢带生产中很常见,这是由于制造制约因素造成的.
- 准确检测这些缺陷对于质量控制和材料完整性至关重要.
研究的目的:
- 为了提高钢带表面缺陷检测的准确性.
- 引入一个新的算法,YOLOv5s-SDF,以改善缺陷识别.
主要方法:
- 通过将ShuffleAttention和DyHead集成到YOLOv5s框架中,开发了YOLOv5s-SDF算法.
- 纳入焦点-CIoU损失以解决低质量样本的影响.
- 该算法在NEU-DET数据集上进行了评估.
主要成果:
- YOLOv5s-SDF获得了69.37%的精度,74.21%的回忆,76.32%的F1得分和38.60%的mAP.
- 拟议的算法显示了与基线YOLOv5s模型相比的性能改善.
结论:
- YOLOv5s-SDF算法有效地提高了条形钢的表面缺陷检测精度.
- 整合ShuffleAttention和Focal-CIoU损失有助于提高性能.
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