摘要
针对内河枢纽航道表面碍航流态实时在线监测技术难题,进行了融合深度学习与多模态数据增强的智能识别系统设计研究,旨在突破传统测量技术无法实时在线监测的瓶颈,提升通航安全保障能力。研究设计了包含图像采集和预处理、目标检测与识别、流速计算和流态分类、数据可视化等模块的表面流态智能识别系统,并提出3项核心算法:基于深度学习的大尺度粒子图像测速算法,通过LSPIV计算流体速度场,并用BP神经网络优化计算结果,使测量相对误差降低至3.48%;基于YOLO-BP网络的多阶段风险感知算法,结合YOLOv5目标检测与BP神经网络分类,实现流态碍航风险动态评估,准确率达98.34%;面向反光、雨雾等干扰的特殊工况数据增强算法,采用小波分析、稀疏编码与暗通道理论,有效提升了图像识别质量。通过长江三峡—葛洲坝枢纽河段实测验证,系统单点监测范围约400 m×500 m,支持7×24 h连续运行,可为内河枢纽航道提供高精度、全天候的流态监测技术支撑,对推动智能航运技术发展具有借鉴意义。
To the technical challenge of real-time online monitoring of surface obstruction flow in inland waterway hubs,this paper conducts research on the design of an intelligent recognition system that integrates deep learning and multimodal data augmentation.The aim is to break through the bottleneck of traditional measurement techniques that cannot monitor in real-time online and improve navigation safety and security capabilities.A surface flow intelligent recognition system is designed,which includes modules such as image acquisition and preprocessing,object detection and recognition,flow velocity calculation and flow classification,and data visualization.Three core algorithms are proposed:a large-scale particle image velocimetry algorithm based on deep learning,which calculates the fluid velocity field through LSPIV and optimizes the calculation results using BP neural network,reducing the relative measurement error to 3.48%.A multi-stage risk perception algorithm based on YOLO-BP network,combined with YOLOv5 object detection and BP neural network classification,achieves dynamic assessment of flow obstruction risk with an accuracy rate of 98.34%.A special working condition data augmentation algorithm for interference such as reflection,rain and fog,using wavelet analysis,sparse coding and dark channel theory,effectively improves image recognition quality.Through actual testing and verification of the Three Gorges and Gezhouba hub section of the Yangtze River,the single point monitoring range of the system is about 400 m×500 m,supporting continuous operation for 7×24 h.It can provide high-precision,all-weather flow monitoring technology support for inland waterway hubs and has reference significance for promoting the development of intelligent shipping technology.
作者
梁锴
李明伟
韩越
田一博
LIANG Kai;LI Mingwei;HAN Yue;TIAN Yibo(Three Gorges Navigation Authority,Yichang 443000,China;Harbin Engineering University,Harbin 150000,China)
出处
《水运工程》
2025年第11期190-196,224,共8页
Port & Waterway Engineering
基金
长江航务管理局重点科技项目(2024-CHKJ-007)
长江三峡通航管理局A类科技项目(KJ2022-02A)。
关键词
流态识别
流速测量
实时监测
智能航运
flow state recognition
flow velocity measurement
real time monitoring
intelligent shipping
