海上监督的混合框架:通过船舶行为和专家规则检测非法活动的融合
Vinicius D do Nascimento1,2, Tiago A O Alves3, Claudio M de Farias1
1Systems Engineering and Computer Science Graduate Program (PESC)/COPPE, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro 21941-972, RJ, Brazil.
Sensors (Basel, Switzerland)
|September 14, 2024
概括
这项研究引入了一个新的框架,用于通过导航行为和专家知识来检测非法海事活动. 该系统在识别非法捕捞和可疑活动方面取得了很高的准确性,加强了海上监视.
科学领域:
- 海事监督部门的监督工作
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 全球海上交通对贸易至关重要,但在安全,环境保护和防止非法活动方面面临挑战.
- 现有的海上监控方法往往在数据整合和将专家知识转化为可行的规则方面扎.
- 自动识别系统 (AIS) 提供了有价值的数据,但需要复杂的分析来检测复杂的非法活动.
研究的目的:
- 通过将导航行为模型与专家知识相结合,开发和验证用于检测非法海事活动的新型框架.
- 创建一个可扩展和高效的系统来对海上活动进行分类,包括非法捕鱼,可疑和异常行为.
- 解决数据标签的局限性,并将数据驱动的方法与专家见解相结合,以提高海事领域的认识.
主要方法:
- 基于联合实验室主任 (JDL) 模型开发了一个五级框架,整合了来自多个来源的数据.
- 结合主动学习的堆组合模型被用来解决数据稀缺问题,并将数据驱动的检测与专家知识结合起来.
- 合成和现实世界的自动识别系统 (AIS) 数据集被用于训练和评估检测模型.
主要成果:
- 该框架实现了高准确性,99%用于检测非法捕捞,92%用于可疑活动.
- 该系统显著减少了需要由海事专家进行手动检查的需求.
- 专家的默契知识被有效地转化为明确的,以模型为导向的规则,使得海事领域的认识能够不断更新.
结论:
- 开发的框架提供了一个可扩展和有效的解决方案,用于加强海上监视和检测非法活动.
- 将人工智能驱动的行为分析与专家知识相结合,为海上安全提供了强大的方法.
- 这项工作在将专家见解转化为海事领域的自动检测系统方面取得了重大进展.
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