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强大的追踪器:集成CPM-YOLO和BOTSORT用于跨模式船舶跟踪
1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
这项研究引入了使用YOLOv8和BOTSORT进行重新识别的先进海上船舶跟踪系统,提高了在具有挑战性的海洋条件下检测准确度和稳定性. 该方法在不影响实时处理速度的情况下实现了高性能.
科学领域:
- 计算机视觉 计算机视觉
- 海事监督部门的监督工作
- 对象跟踪是指对象的跟踪.
背景情况:
- 海上环境对船舶检测和跟踪提出了复杂的挑战,包括密集的目标,尺度变化和遮蔽.
- 在这些苛刻的条件下,现有的方法往往在准确性和稳定性方面扎.
研究的目的:
- 开发一种高精度和强大的多对象跟踪方法,用于海上船舶的检测和跟踪.
- 改善小型船舶的检测,并在复杂的海洋场景中提高跟踪稳定性和可靠性.
主要方法:
- 一个基于YOLOv8的增强型探测器,具有轻量级的功能增强和注意力机制,用于改进检测.
- 为了稳定的船舶协会,采用了将BOTSORT与基于OSNet的重新识别 (ReID) 模型相结合的跟踪框架.
- 该方法在近红外在岸 (NIR) 和可见在岸 (VIS) 数据集上进行了评估.
主要成果:
- 与基线YOLOv8.8相比,拟议的方法在NIR数据集上的精度,回忆,mAP@0.5和mAP@0.5:0.95显著改善,与基线YOLOv8.95相比.
- 它在VIS数据集上实现了卓越的检测准确性和稳定性,超过了最先进的方法.
- 该框架在轨迹连续性,阻塞处理和多对象跟踪任务中的身份保存方面显示出优势.
结论:
- 开发的方法为实际的海事应用提供了检测准确性,跟踪稳定性和模型效率之间的有利平衡.
- 它实现了高性能,包括具有竞争力的推断速度 (188 FPS在NIR上,187 FPS在VIS上),而不牺牲实时功能.
- 这种方法表现出强大的跨模式概括能力,在不同的视觉条件下证明有效.
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