对轻量级目标检测方法的比较研究,用于基于无人驾驶飞行器的道路危险调查
Feifei Xu1, Yan Wan1, Zhipeng Ning2,3
1School of Civil and Transportation Engineering, Zhejiang Engineering Research Center of Digital Road Construction Technology, Ningbo University of Technology, Ningbo 315211, China.
Sensors (Basel, Switzerland)
|September 28, 2024
概括
YOLO-RDD模型擅长使用无人驾驶飞行器 (UAV) 检测道路异常,为道路危险检测提供高精度和显著的计算效率.
科学领域:
- 计算机科学 计算机科学
- 土木工程 土木工程是指土木工程.
- 遥感 遥感 遥感 遥感
背景情况:
- 无人驾驶飞行器 (UAV) 提供了道路异常检测的潜力,但面临着离散空间覆盖和计算需求的挑战.
- 现有的道路异常检测算法需要改进,以提高无人机应用程序的稳定性和效率.
研究的目的:
- 评估和比较各种深度学习模型的性能,用于使用无人机图像检测道路交通事故.
- 确定最有效的模型,以准确和计算高效地识别道路异常.
主要方法:
- 采用K-means聚类来确定最佳的先前箱.
- 几个深度学习模型,包括Faster R-CNN,YOLOX-s,YOLOv5-s,YOLOv7-tiny,YOLO-MobileNet和YOLO-RDD,都是在无人机收集的道路图像数据上开发和训练的.
- 通过使用平均平均精度 (mAP) 和平均精度 (AP) 等指标在0.5.5的欧盟交叉点 (IoU) 门上评估性能.
主要成果:
- YOLO-RDD模型表现出最高的性能,实现了0.701的mAP,并在检测四种类型的道路困境方面都表现出色.
- 在坑洞检测方面,YOLO-RDD特别成功 (AP为0.790),与较小的裂相比,更好地识别了显著的痛苦.
- 与YOLOv7-tiny相比,YOLO-RDD实现了85%的计算减少,同时保持了高精度.
结论:
- YOLO-RDD模型是基于无人机的道路危险探测的高效和计算效率的解决方案.
- 虽然YOLO-RDD显示出前景,但可能需要进一步的改进,以最佳地检测小的道路缺陷,如小裂.
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