每艘船都很重要:基于神经网络的海上交通计数系统
Miro Petković1, Igor Vujović1, Nediljko Kaštelan1
1Faculty of Maritime Studies, University of Split, Ruđera Boškovića 37, 21000 Split, Croatia.
Sensors (Basel, Switzerland)
|August 12, 2023
概括
本研究介绍了使用摄像头和人工智能进行海上交通监控的实时船舶计数系统. 该系统实现了高准确性,并比传统方法捕获的数据要多得多,增强了港口运营和研究.
科学领域:
- 计算机视觉 计算机视觉
- 海事技术 在海事技术.
- 人工智能的人工智能
背景情况:
- 传统的海上交通监控系统,如AIS和VTS缺乏全面的数据,特别是在不同的港口环境中.
- 地中海港口面临着现有系统的挑战,因为船只类型和运营复杂性各不相同.
- 准确的海上交通数据对于高效的港口管理和深入的海上研究至关重要.
研究的目的:
- 开发和评估实时船只计数系统,使用陆基摄像头加强港口海上交通监控.
- 通过克服现有系统的局限性,改善用于海洋研究的数据采集.
- 为在复杂的海上环境中提供强大而准确的船舶检测和分类解决方案.
主要方法:
- 实施YOLOv4卷积神经网络 (NN),在新型SPSCD数据集上进行训练,用于将船舶分为12个类别.
- 使用卡尔曼跟踪器与匈牙利分配 (HA) 算法相结合,用于多目标船只跟踪.
- 纳入稳定性评估,以减轻来自非船舶物体的误报,并提高跟踪可靠性.
主要成果:
- 该系统的平均计数准确率为97.76%.
- 实现平均处理速度为每秒31.78,表明高效率.
- 与传统的自动识别系统 (AIS) 方法相比,捕获了386%的海上交通数据.
结论:
- 拟议的基于摄像头的系统为实时海上交通监控提供了快速,稳健和有效的解决方案.
- 该系统显著提高了数据收集能力,为海事研究和港口运营提供了宝贵的见解.
- 这项技术为提高海上交通数据分析的全面性提供了巨大的潜力.
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