使用改进的YOLOv5进行夜间指甲检测,以提高道路安全的精度
Haijian Wang1, Ziliang Hu1, Han Mo1
1Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin, 541004, Guangxi, China.
Scientific reports
|February 12, 2025
概括
这项研究引入了一种改进的YOLOv5系统,用于在夜间检测道路钉,提高准确性和效率,以实现更安全的道路. 该系统实现了高的识别和检索成功率,证明了显著的实际价值.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 道路安全工程 道路安全工程
背景情况:
- 夜间道路维护对检测和清除钉子等危险物具有挑战.
- 现有的检测系统缺乏有效的夜间操作所需的准确性和效率.
研究的目的:
- 开发一个增强的夜间指甲检测系统,以改善道路危险的识别和检索.
- 提高道路钉检测和移除系统的准确性和效率.
主要方法:
- 一个改进的YOLOv5模型,包含修改后的C3模块,重新参数化的特征金字塔网络 (RepGFPN) 和最佳运输赋值损失 (OTALoss).
- 在NVIDIA Jetson Orin Nano设备上部署,配备立体相匹配算法,用于同步识别和定位.
- 与基于双眼视觉的电磁检索系统和用于自动操作的环标记系统的集成.
主要成果:
- 增强的YOLOv5模型实现了91.5%的平均识别精度,比原始YOLOv5.5有11.3%的改进.
- 在120度视野中,定位误差保持在2.0厘米以内.
- 完整的机器人控制系统展示了超过98%的检索和标记精度.
结论:
- 开发的系统显著提高了夜间道路钉检测和检索能力.
- 该系统提供了一种实用且有价值的解决方案,通过有效的危险消除来提高道路交通安全.
- 先进的人工智能模型和机器人系统的整合显示出未来道路维护技术的前景.
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