陶圆盘的表面缺陷检测基于改进的YOLOv5s
Haipeng Pan1, Gang Li1, Hao Feng1
1School of Mechanical and Electrical Engineering, Jingdezhen Ceramic University, Jingdezhen, 333403, China.
Heliyon
|July 12, 2024
概括
这项研究增强了YOLOv5s算法用于陶磁盘表面缺陷检测,提高了小型和多样化的缺陷的准确性和速度. 与以前的版本相比,改进的模型提供了更好的概括性和实时性能.
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:
- 陶盘表面缺陷检测面临着小缺陷,尺寸变化和定位准确性的挑战.
- 现有的方法难以一致识别各种表面异常.
研究的目的:
- 增强YOLOv5s算法,以更准确,更有效地检测陶磁盘表面缺陷.
- 解决检测小缺陷的局限性,提高定位精度.
主要方法:
- 改进了YOLOv5s算法,修改了框架结构,以更好地概括.
- 集成ECA注意力机制以提高小目标检测准确度.
- 在相同的实验条件下对YOLOv3和YOLOv4模型进行比较分析.
主要成果:
- 精度,F1分数和mAP分别增长了3.1%,3%和4.5%. 这两种类型的分数均有所增加.
- 对于裂,损坏,渣和点位缺陷的准确性分别提高了0.2%,4.7%,5.4%和1.9%.
- 检测速度从232/秒增加到256/秒,超过YOLOv3和YOLOv4.
结论:
- 增强的YOLOv5s算法显著改善了陶磁盘的表面缺陷检测.
- 该模型在识别小缺陷和实现实时检测方面表现出卓越的性能.
- 这一进步为陶材料的工业质量控制提供了更强大的解决方案.
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