基于YOLOv5s-DeepSort组合的端口终端移动识别
Chengzhi Wang1, Donghong Chen1, Zhen Liu1
1School of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing, China.
PloS one
|July 10, 2025
概括
这项研究增强了YOLOv5s-DeepSORT用于港口和码头视频分析,改善了在具有挑战性的条件下识别目标和跟踪轨迹. 先进的模型显示对象检测和多目标跟踪的准确性显著提高.
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:
- 港口和码头环境对视频分析提出了挑战,原因是尺度变化,遮蔽和复杂的背景.
- 现有的物体检测和跟踪模型在这些动态设置中扎着降低定位准确度.
研究的目的:
- 开发一个增强的YOLOv5s-DeepSORT模型,以改善港口和码头视频图像中的目标负载识别和轨迹跟踪.
- 在复杂的码头环境中提高对象检测和跟踪系统的适应性和稳定性.
主要方法:
- 将多尺度卷积集成到YOLOv5s中,以增强多尺度对象检测.
- 整合一个高效的金字塔细分注意力 (EPSA) 网络,以改善多尺度特征融合.
- 应用分布式排序损失函数来解决类不平衡和背景变化.
主要成果:
- 改进的YOLOv5s模型通过多尺度卷积实现了平均平均精度 (mAP) 提高0.4%.
- 由于EPSA网络的整合,mAP@0.5:0.95.5.的增长率增加了1.2%.
- 在实施分布式分类损失后,多目标跟踪精度 (MOTA) 提高了3.1%.
- 该模型在自建数据集上显示了90.9%的平均准确性和92.2%的检测准确性.
结论:
- 提议的增强型YOLOv5s-DeepSORT模型显著提高了定位准确性和在港口和码头环境中的适应性.
- 多尺度卷积,EPSA网络和分布式分类损失的组合为目标识别和跟踪提供了强大的解决方案.
- 这项研究为开发海上物流领域先进的监控和运营监控系统提供了宝贵的参考.
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