用无人机检查系统对山区道路进行自主裂纹检测.
Xinbao Chen1,2, Chenxi Wang2, Chang Liu2
1Sanya Research Institute, Hunan University of Science and Technology, Sanya 572025, China.
Sensors (Basel, Switzerland)
|July 27, 2024
概括
本研究介绍了一种无人机 (UAV) 系统,用于在山区自动检测道路裂. 该系统使用先进的导航和改进的YOLOv8算法进行高效和准确的检查.
科学领域:
- 土木工程 土木工程是指土木工程.
- 机器人技术 机器人技术 机器人技术
- 计算机视觉 计算机视觉
背景情况:
- 道路裂降低了基础设施的可用性和安全性,特别是在具有挑战性的山地地形上.
- 传统的检查方法是低效的,劳动密集型,不适合复杂的道路几何形状.
- 现有的多功能检测车辆是昂贵的,对于山区环境来说不切实际.
研究的目的:
- 开发一个定制的无人驾驶飞行器 (UAV) 检查系统,用于在山区自动检测道路裂.
- 在GPS拒绝或GPS漂移的场景中增强自主导航能力.
- 通过使用优化的深度学习模型,提高裂纹检测的准确性和效率.
主要方法:
- 实施滑动窗 (SWM) 方法,以便在具有挑战性的地形中进行可靠的路径规划和自主导航.
- 开发了一种改进的MRC-YOLOv8算法,用于从无人机图像中准确,实时地检测道路裂.
- 进行全面的实验以验证系统性能,准确性和效率.
主要成果:
- 与SWM集成的无人机系统在GPS拒绝的山地地形中展示了有效的实时路线规划,具有较低的定位漂移 (2.75%) 和扫描误差 (0.33米).
- 在破裂检测方面,MRC-YOLOv8算法获得了高F1-Score的87.4%和平均平均精度 (mAP) 的92.3%.
- 与原始YOLOv8.8相比,MRC-YOLOv8模型显示了显著的参数体积减小 (0.19×10^6),表明了更轻便和高效的设计.
结论:
- 拟议的无人机检查系统提供了一种可行,高效和准确的解决方案,用于在山区进行日常道路裂监测.
- 集成的SWM和MRC-YOLOv8算法显著提高了复杂地形的自主导航和检测能力.
- 这项技术为改善道路基础设施管理和在充满挑战的环境中的安全提供了宝贵的指导.
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