基于改进的YOLOv5的自动化带铺设和绕的表面缺陷检测
Liwei Wen1, Shihao Li1, Jiajun Ren2
1College of Material Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Materials (Basel, Switzerland)
|August 12, 2023
概括
改进的YOLOv5模型增强了复合材料的实时表面缺陷检测,提高了准确性和速度. 这减少了手工干预和在自动化带铺设和绕过程中的重工.
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:
- 自动化复合材料制造面临着表面缺陷检测的挑战,包括低精度,缓慢的速度和高错误率.
- 现有的方法通常需要手工检查,导致成本增加和生产延误.
- 实时,高精度的缺陷检测对于自动化带铺设和绕的质量控制至关重要.
研究的目的:
- 开发一种改进的YOLOv5算法,用于高精度,实时地表缺陷检测复合材料.
- 为了减少在自动化复合材料制造过程中需要人工干预和重新加工的需要.
- 在准确性,速度和可靠性方面提高缺陷检测系统的性能.
主要方法:
- 一个增强的YOLOv5模型,结合了坐标注意力 (CA) 机制,以改进特征提取.
- 整合一个单独的CA结构以优化检测速度.
- 实施SIoU (SCYLLA-Intersection over Union) 损失函数,以提高目标盒回归稳定性.
- 使用软-SIoU-NMS来更好地检测重叠缺陷,取代传统的NMS.
主要成果:
- 改进的YOLOv5模型实现了97.2%的平均平均精度 (mAP),超过了95%的精度值.
- 检测速度增加到每秒72.1 (FPS),从66.7 FPS上升.
- 改进的模型在自动化过程中在预浸复合材料的表面缺陷检测方面表现出卓越的性能.
结论:
- 建议改进的YOLOv5模型有效地解决了复合材料当前表面缺陷检测的局限性.
- 集成CA,SIoU损失和软SIoU-NMS显著提高了检测准确度和速度.
- 这一进步促进了实时在线检测,最大限度地减少了自动化制造中的手动检查和重工.
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