用无人机视频检测车辆识别和驾驶信息,基于改进的YOLOv5-DeepSORT算法
Binshuang Zheng1,2, Jing Zhou2, Zhengqiang Hong3
1Research and Development Center of Transport Industry of New Generation of Artificial Intelligence Technology, Zhejiang Scientific Research Institute of Transport, No. 705 Dalongjuwu Rd., Hangzhou 311305, China.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究使用无人驾驶飞行器 (UAV) 和经过重新训练的YOLOv5算法来准确捕获车辆驾驶数据. 这种方法通过考虑现实世界的驾驶习惯来增强车辆安全分析.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 运输安全运输安全
背景情况:
- 使用理想驾驶员模型的传统车辆驾驶模拟不足以评估坡道滑行阻力和安全性.
- 现实世界的驾驶习惯显著影响车辆的稳定性和安全性,需要更准确的数据收集方法.
研究的目的:
- 开发一种从无人机 (UAV) 的角度收集和提取车辆驾驶信息的方法.
- 提高车辆检测和轨迹分析的准确性和速度,以建立现实的驾驶员模型.
主要方法:
- 使用无人机 (UAV) 来捕获实时车辆驾驶视频数据.
- 在Google协作平台上修改并重新训练了"你只看一次"第5版 (YOLOv5) 算法,使用Python 3.7.12.12在Google协作平台上进行了修改和重新训练.
- 将训练好的YOLOv5模型集成到DeepSORT算法中,取代了Faster R-CNN,用于增强车辆检测和信息提取.
- 采用编码来提取和平滑车辆轨迹坐标,并使用差方法来计算实时速度.
主要成果:
- 经过重新训练的YOLOv5算法实现了令人满意的精度 (P) 和回忆率 (R),F1得分为0.86.
- 在70个训练时代后,YOLOv5模型的损失函数稳定在低水平,表明有效的学习.
- 增强的DeepSORT算法与YOLOv5提高了检测准确度和速度,从无人机录像中提取车辆驾驶信息.
结论:
- 拟议的方法有效地从无人机角度提取关键的车辆驾驶信息,包括轨迹和速度.
- 这种方法为构建更准确的真实驾驶员模型提供了基础,这对于评估车辆安全性和坡道滑行阻力至关重要.
- 无人机和先进的计算机视觉算法的集成为智能运输系统和安全分析提供了有前途的解决方案.
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