基于改进的YOLOv5s和DeepSORT的道路行人检测和跟踪算法
Guofeng Qin1,2, Rongting Pan3,2, Yi Deng4
1Teachers College for Vocational and Technical Education, Guangxi Normal University, Guilin, China.
PloS one
|November 4, 2025
概括
这项研究引入了一种改进的行人检测和跟踪算法,使用增强的YOLOv5s和DeepSORT. 新方法显著提高了准确性和跟踪稳定性,特别是在密集交通场景中的小型物体.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 步行者检测和跟踪面临的挑战是精度低,错误率高,在道路上密集,封闭和小物体场景中稳定性差.
- 现有的算法在这些困难条件下难以保持性能,需要改进的方法.
研究的目的:
- 开发一个强大的行人检测和跟踪算法,克服精度的局限性,错误检测和跟踪稳定性.
- 为了提高性能,特别是在交通环境中用于密集的遮蔽和小物体检测.
主要方法:
- 改进了YOLOv5s检测网络,包括焦点-EIoU损失,小物体 (SO) 检测层和多头自我注意 (MHSA) 机制.
- 增强的DeepSORT跟踪框架与轻量级的ShuffleNetV2网络用于外观特征提取.
- 集成改进的YOLOv5s与修改的DeepSORT进行全面的行人跟踪.
主要成果:
- 改进的YOLOv5s实现了80.8%的mAP0.5和49.7%的mAP0.5:0.95,超过了原始YOLOv5s的4.4%和3.9%.
- 增强的YOLOv5s-DeepSORT实现了50.7%的MOTA和77.3%的MOTP,身份交换机减少了11.3%.
- 模型尺寸缩小至原始的20%,增强可移植性而不会影响准确性.
结论:
- 提议改进的YOLOv5s-DeepSORT算法在行人检测和跟踪方面提供了卓越的性能,特别是在挑战密集和小物体场景时.
- 该方法在准确性,跟踪稳定性和效率方面取得了显著的改进,使其适用于现实世界的交通应用.
- 增强的算法是强大的,能够有效地跟踪不同大小的目标,解决以前方法的关键局限性.
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