实现可持续的干净港口:基于语义细分和边缘设备的烟雾不透明度测量
Min-Ho Park1, Jae-Hyuk Choi2, Won-Ju Lee3
1Division of Marine Engineering, Korea Maritime and Ocean University, Busan. 49112, Republic of Korea; Interdisciplinary Major of Maritime and AI Convergence, Korea Maritime and Ocean University, Busan. 49112, Republic of Korea.
Journal of environmental management
|September 1, 2025
概括
本研究介绍了使用DeepLab v3+m模型在边缘设备上自动检测船舶烟雾不透明性的AI框架. 系统准确地测量和报告烟雾不透明度,提高环境监测.
科学领域:
- 环境科学
- 人工智能
- 海洋工程
背景情况:
- 手动测定船舶烟雾的不透明度是劳动密集型的,容易出现错误.
- 准确监测船舶排放对于环境保护和合规性至关重要.
研究的目的:
- 开发和验证用于测量船舶烟雾不透明性的自动化系统.
- 在边缘设备上实现人工智能 (AI) 框架,用于实时分析.
- 为实地实施提出政策框架.
主要方法:
- 四个深度学习模型 (W-Net,DeepLab v3+m) 的比较,用于烟雾图像的语义细分.
- 在使用开放神经网络交换 (ONNX) 格式的边缘设备上部署优化的DeepLab v3+m模型.
- 图像捕获,烟雾不透明度测量,报告生成和电子邮件传输的算法开发.
主要成果:
- 与传统模型相比,DeepLab v3+m 在语义细分和本地化方面表现出卓越的性能.
- 边缘设备系统成功捕获图像,生成准确的烟雾不透明报告,并通过电子邮件传输.
- 开发的系统在港口环境中实现了船舶烟雾不透明的无自动检测.
结论:
- 基于人工智能的框架,特别是边缘设备上的DeepLab v3+m,为船舶烟雾不透明度监测提供了可靠和自动化的解决方案.
- 拟议的政策框架有助于将这项技术纳入现场操作实践.
- 这种系统提高了海上排放环境监测的效率和准确性.
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