基于EDC-YOLO的CFRP中流检测缺陷的分类,定位和量化
Rongyan Wen1, Chongcong Tao1, Hongli Ji1
1College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210000, China.
Sensors (Basel, Switzerland)
|October 26, 2024
概括
本研究介绍了一种改进的流YOLO (EDC-YOLO) 模型,用于检测和量化碳纤维增强塑料 (CFRP) 的缺陷,使用流非破坏性测试 (ECNDT). 改进后的模型显著提高了工业应用中缺陷检测的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 非破坏性测试是一种非破坏性测试.
- 人工智能的人工智能
背景情况:
- 准确的缺陷检测对于碳纤维增强塑料 (CFRP) 部件的可靠性至关重要.
- 厄迪电流非破坏性测试 (ECNDT) 是检查这些材料的关键技术.
- 现有的方法在识别和量化不同类型的缺陷方面可能面临挑战.
研究的目的:
- 调查常见的CFRP缺陷 (裂,分层,冲击损伤) 的识别和测量.
- 增强你只看一次 (YOLO) 模型,以提高ECNDT的性能.
- 开发一个改进的流YOLO (EDC-YOLO) 模型用于缺陷量化.
主要方法:
- 利用你只看一次 (YOLO) 模型作为缺陷检测的基础.
- 集成的基于变压器的自我注意机制和可变形的卷积子模块,以解决多尺度特征限制.
- 集成的CBAM用于全球特征提取和Wise-IoU损失功能以提高性能.
- 开发了改进的伊迪电流YOLO (EDC-YOLO) 模型.
主要成果:
- EDC-YOLO模型在识别和量化CFRP的裂,分层和冲击损伤方面表现出有效性.
- 模型的改进导致了4.4%的增长 mAP50 缺陷检测.
- 该研究提供了关于撞击损伤特征和能量水平之间的相关性的见解.
结论:
- EDC-YOLO模型提供了一个可靠的解决方案,用于CFRP的工业ECNDT中的缺陷识别和量化.
- 集成的注意力机制和损失功能显著提高了检测准确度.
- 这种人工智能驱动的方法提高了CFRP检查的可靠性和效率.
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