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基于视频的识别和预测技术,用于在桥梁区域的稳定船舶轨迹

Woqin Luo¹, Ye Xia¹, Tiantao He^1,2

¹Department of Bridge Engineering, School of Civil Engineering, Tongji University, Shanghai 200092, China.

Sensors (Basel, Switzerland)

|January 23, 2024

概括

这项研究引入了船舶轨迹识别的新框架,提高了船舶跟踪的准确性和减少了错误. 该系统通过对潜在碰撞提供实时警报来提高海上安全.

科学领域:

海上安全的航行.
人工智能的人工智能
船舶交通管理船舶交通管理

背景情况:

在全球范围内,船舶与桥梁的碰撞事件正在增加,这凸显了对更好的船舶轨道识别的需求.
目前使用YOLO与DeepSORT/Bytetrack的方法在检测准确性和边界采集方面存在困难,导致错误阳性和错过检测.

研究的目的:

开发一种用于准确识别和预测船舶轨迹的新型框架.
为了减轻船舶跟踪中虚假阳性和负性的问题.
通过实时异常检测和警告,增强海上安全.

主要方法:

推出了Co-tracker,这是一个长期序列多功能点跟踪方法,用于准确的轨迹跟踪.
利用长短期记忆 (LSTM) 和图表注意力神经网络 (GAT) 进行轨迹预测和异常检测.
统计计算的特征点集群转换,以提高追踪精度.

主要成果:

与使用DeepSORT的YOLO相比,提出的方法显示出卓越的准确性,并捕获了与YOLO相比的重要航向角度特征.
在船舶检测和跟踪中有效减少虚假阳性和虚假阴性.
在宁波三河地区成功提供实时船舶集团轨迹和预测.

结论:

新的框架显著提高了船舶交通管理的效率,并减少了碰撞风险.
该系统在复杂,多目标和广域场景中增强了安全导航.
对潜在的禁区入口发出及时的视听警告,改善了实时警报功能.

关键词:

这就是GAT GAT的意思.变压器的变压器是一个变压器.预测船只轨迹的预测.船舶轨迹跟踪跟踪系统船舶与桥梁碰撞的碰撞

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