TCQI-YOLOv5:一个终端接质量缺陷检测网络
Yingjuan Yu1, Dawei Ren1, Lingwei Meng1
1School of Energy and Mining Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
这项研究介绍了TCQI-YOLOv5,这是一种用于汽车电线束中自动终端结质量检查 (TCQI) 的AI模型. 它显著提高了缺陷检测的准确性和速度,以实现可靠的信号传输.
科学领域:
- 汽车工程 汽车工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 汽车电线束依赖于高品质的终端结,以确保信号完整性.
- 手动检查终端结质量是低效的,容易出现错误.
研究的目的:
- 开发一个用于准确和高效的终端结质量检查 (TCQI) 的自动化系统.
- 通过利用先进的深度学习技术来改进现有方法.
主要方法:
- 开发了一个改进的YOLOv5模型 (TCQI-YOLOv5).
- 用C2f结构,FasterNet和高效多尺度注意力 (EMA) 增强了特征提取.
- 使用SIOU损失函数来改善边界框定位.
主要成果:
- TCQI-YOLOv5实现了98.3%的平均精度 (mAP).
- 该模型在检测微妙的缺陷方面表现出卓越的准确性,例如浅的绝缘纹.
- 检测速度满足实时工业应用的要求.
结论:
- TCQI-YOLOv5为自动化终端结质量检查提供了一种高效准确的解决方案.
- 该模型显示出在汽车行业实际部署的巨大潜力.
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