一项基于视频图像和YOLO算法对公共汽车乘客登机和下车检测和识别的研究
Wei Xu1, Yushan Zhao1, Xiaodong Du1
1College of Transportation, Qingdao Campus, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|March 14, 2026
概括
这项研究引入了改进的YOLOv8算法,用于使用视频准确检测公共汽车乘客登上和下车. 这增强了来源-目的地数据收集,这对于智能城市交通系统至关重要.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 准确的乘客起点-目的地 (OD) 数据对于智能公共交通和智能城市发展至关重要.
- 传统的数据收集方法,如手动调查和智能卡数据,在准确性和完整性方面存在重大限制.
- 现有的物体检测算法在公共汽车环境中难以应对高密度,封闭和尺度变化的乘客的挑战.
研究的目的:
- 开发一个增强的物体检测模型,以准确识别公共汽车乘客登机和下车事件.
- 为了提高YOLO算法的性能,在复杂的车载场景中,包括车内堵塞和不同的乘客尺度.
- 为实时乘客流量数据收集提供强大的解决方案,以支持智能运输系统.
主要方法:
- 这项研究提出了一种改进的YOLOv8n模型,其中包含一个DAC2f结构 (可变形的注意力+C2f),以更好地提取特征和抑制背景.
- 引入了一个SWD-PAN模块,用于有效的双向跨尺度特征交互,以处理尺度变化.
- 使用WIoUv3来优化样本权重,特别是对于小型目标和非标准的乘客姿势.
- 增强的YOLOv8模型与DeepSORT集成,以提高多对象跟踪稳定性.
主要成果:
- 与基线相比,改进的YOLOv8模型在精度 (+3.68%),回忆 (+5.12%) 和mAP (+6.26%) 中取得了显著的改进.
- 该模型满足公共汽车乘客检测的实时处理要求.
- 与DeepSORT的集成导致MOTA为31.24% (比YOLOv8n高出2.6%) 和MOTP为88.06%,有效减少轨迹断裂和ID切换.
结论:
- 提议的增强的YOLOv8算法有效地解决了公共交通中的传统OD数据收集方法的局限性.
- 这项研究为精细管理智能公共交通和优化智能城市交通提供了坚实的技术基础.
- 开发的系统为实时,准确的乘客流量分析提供了可行的解决方案,有助于实现更智能的城市流动.
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