一个改进的YOLOv5n算法用于检测工业部件的表面缺陷
Jia-Hui Tian1,2, Xue-Feng Feng3, Feng Li3
1College of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China.
Scientific reports
|March 22, 2025
概括
本研究引入了用于工业表面缺陷检测的增强YOLOv5n算法,通过优化特征提取和信息融合来提高准确性. 升级后的模型在NEU-DET数据集上的平均精度提高了4.3%.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 工业自动化 工业自动化
背景情况:
- 精确的工业表面缺陷检测是关键的,但由于缺陷大小小和模糊的特征,具有挑战性.
- 现有的方法经常遭受错误检测,影响质量控制和制造效率.
研究的目的:
- 提出一个改进的YOLOv5n算法,用于增强工业表面缺陷检测.
- 解决当前方法在准确识别微妙表面缺陷方面的局限性.
主要方法:
- 在骨干中实现了DSConv-CA模块,以实现高级特征提取.
- 在子中集成金色YOLO结构以改进信息融合.
- 利用SIoU损失函数来提高界限框回归精度.
主要成果:
- 改进的YOLOv5n算法在NEU-DET数据集上实现了75.3%的平均平均精度 (mAP).
- 与原始YOLOv5n模型相比,mAP增加了4.3%.
- 在检测小型和模糊的工业表面缺陷方面展示了增强的能力.
结论:
- 拟议的修改大大提高了YOLOv5n用于工业表面缺陷检测的性能.
- 集成DSConv-CA,Gold-YOLO和SIoU损失提供了一个强大的解决方案,用于高精度的缺陷识别.
- 这种先进的算法有助于在工业制造中实现更可靠的质量控制.
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