摘要
[目的]旨在提出对航行于关键广阔水域内的船舶进行准确识别和定位的改进方法。[方法]运用视频监控的优点,综合采用基于背景差分算法的运动目标检测方法与基于深度学习算法的图像表象特征识别方法,结合目标的运动特征和图像表象特征,实现多维度广域船舶识别的功能,并对水纹降噪、多级运动检测、航道监控图像窗口分割检测等方法进行改进,进一步提高航行监控系统的船舶识别准确率。[结果]现场航道监控验证结果表明,采用所提改进方法可以准确识别航道监控画面中任意类型和尺度的船舶,且使用常规摄像头即可实现半径3 km范围内的船舶识别、定位效果。[结论]所提方法具有监控范围广、船舶类型全覆盖、自动目标识别、抗干扰能力强等优点。
[Objective]The aim of this paper is to proposes methods for better recognizing and positioning ships sailing in critical and wide-area waterways during monitoring operation.[Methods]Based on video surveillance technology,the joint use of the motion and appearance features of ship target is carried out to realize a wide-area multi-dimensional recognition function via the combination of background subtraction based moving object detection algorithm and deep learning based target recognition algorithm.In addition,the improved approaches including water ripple noise reduction,hierarchical moving object detection and window segmentation of waterway monitoring image are put forward to further improve recognition accuracy.[Results]The field demonstration results show that the improved methods proposed in this paper allow the accurate recognition of a ship of any type or size on the monitoring screen,and the use of conventional cameras can also achieve the recognition and position of a ship navigating a water area within a radius of 3 km.[Conclusions]The improved methods proposed in this study have a range of advantages including widearea monitoring,complete coverage of ship types and sizes,automatic target recognition and robust antiinterference ability.
作者
严荣慧
谢海成
花敏恒
羊箭锋
YAN Ronghui;XIE Haicheng;HUA Minheng;YANG Jianfeng(Wenzheng College,Soochow University,Suzhou 215104,China;School of Electronic and Information Engineering,Soochow University,Suzhou 215006,China)
出处
《中国舰船研究》
CSCD
北大核心
2022年第1期227-234,共8页
Chinese Journal of Ship Research
基金
2021江苏省高等学校自然科学研究面上项目(21KJD510006)
苏州大学文正学院2020年高等教育改革研究课题(2910342520)
2018江苏省高等学校自然科学研究面上项目(18KJD510009)。
关键词
广域航道监控
船舶识别
运动检测
多维特征
YOLO检测算法
wide-area waterway monitoring
ship recognition
motion detection
multi-dimensional features
YOLO detection algorithm
作者简介
严荣慧,女,1993年生,硕士,讲师。研究方向:模式识别与人工智能。E-mail:wzj029@suda.edu.cn;谢海成,男,1990年生,科研助理。研究方向:信号与信息处理。E-mail:14919380932@qq.com;花敏恒,男,1995年生,硕士生。研究方向:图像处理。E-mail:773605595@qq.com;通信作者:羊箭锋,男,1978年生,博士,高级实验师。研究方向:信号与信息处理。E-mail:jfyang@suda.edu.cn。
