Survey of Ship Detection in Video Surveillance Based on Shallow Machine Learning

doi:10.16182/j.issn1004731x.joss.21-FZ0781

Abstract

Abstract: At present, detection of ship targets in video surveillance based on shallow machine learning methods is still attracting attention in the fields of underwater cultural heritage protection, marine aquaculture, maritime traffic, and port management. This paper provides a review and discussion for this kind of ship detection methods. The ship target detection based on video surveillance is divided into five parts according to the key technologies involved: preprocessing, region of interest extraction, target segmentation, ship feature extraction and ship type recognition. According to different functional modules, the core problems involved in them are pointed out, and the core ideas, advantages and disadvantages of representative algorithms in each kind of problems are elaborated. The existing problems and future prospects of ship detection in video surveillance based on shallow machine learning are discussed.

Key words: ship target detection, intelligent video surveillance, shallow machine learning method, maritime security, computer vision

CLC Number:

TP29
TP391.9

Bi Zhenbo, Zhang Shiyou, Yang Hua, Wu Yuanhong. Survey of Ship Detection in Video Surveillance Based on Shallow Machine Learning[J]. Journal of System Simulation, 2021, 33(12): 2792-2807.

References

[1] Schöller F E T, Plenge-Feidenhans M K, Stets J D, et al. Assessing Deep-learning Methods for Object Detection at Sea From LWIR Images[J]. IFAC-PapersOnLine (S2405-8963), 2019, 52(21): 64-71.
[2] Shao Z, Wang L, Wang Z, et al.Saliency-aware Convolution Neural Network for Ship Detection in Surveillance Video[J]. IEEE Transactions on Circuits and Systems for Video Technology (S1051-8215), 2019, 30(3): 781-794.
[3] 臧风妮. 智能视频监控中海面舰船目标的检测算法研究[D]. 青岛: 中国海洋大学, 2014.
Zang Fengni.Research of Ship Detection Algorithms for Intelligent Video Maritime Monitoring[D]. Qingdao: Ocean University of China, 2014.
[4] 李祎承. 内河船舶目标图像识别技术研究[D]. 武汉: 武汉理工大学, 2014.
Li Yicheng.Research of Vessel Target Image Recognition Technology[D]. Wuhan: Wuhan University of Technology, 2014.
[5] Schwendeman M, Thomson J.A Horizon-tracking Method for Shipboard Video Stabilization and Rectification[J]. Journal of Atmospheric and Oceanic Technology (S1520-0426), 2015, 32(1): 164-176.
[6] Karpov A D, Zhilenkov A A, Lisitsa D.The Integration of the Video Monitoring, Inertial Orientation and Ballast Systems for Container Ship's Emergency Stabilization[C]// 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). Piscataway, NJ: IEEE, 2017: 887-891.
[7] Sharma N, Kumar V, Singla S K.Single Image Defogging Using Deep Learning Techniques: Past, Present and Future[J]. Archives of Computational Methods in Engineering (S1886-1784), 2021, 28(5): 1-21.
[8] 颜利斌. 基于机器视觉的内河航道船艇特征提取与识别[D]. 长沙: 湖南师范大学, 2015.
Yan Libin.Feature Extraction and Recognition of Inland Waterway Vessels Based on Machine Vision[D]. Changsha: Hunan Normal University, 2015.
[9] 周丽萍. 动态光视觉的水面目标检测技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
Zhou Liping.Research on Water Surface Target Detection Technology Based on Dynamic Optical Vision[D]. Harbin: Harbin Engineering University, 2018.
[10] Jackson J, Kun S, Agyekum K O, et al.A Fast Single-image Dehazing Algorithm Based on Dark Channel Prior and Rayleigh Scattering[J]. IEEE Access (S2169-3536), 2020, 8: 73330-73339.
[11] Fefilatyev S, Goldgof D, Shreve M, et al.Detection and Tracking of Ships in Open Sea with Rapidly Moving Buoy-mounted Camera System[J]. Ocean Engineering (S0029-8018), 2012, 54: 1-12.
[12] 王汝珣. 基于形状外观的无人船目标识别方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
Wang Ruxun.Research on Target Recognition Method Based on Shape Appearance for Unmanned Ship[D]. Harbin: Harbin Engineering University, 2018.
[13] Su L, Pang D, Liu Z.Panoramic Sea-sky Line Extraction Based on Improved Hough Circle Transform[C]// Chinese Control Conference. Piscataway. NJ: IEEE, 2015: 3690-3694.
[14] 汤伟, 刘思洋, 高涵, 等. 基于改进霍夫变换的水面无人船水界线检测方法[J]. 科学技术与工程, 2018, 18(31): 36-41.
Tang Wei, Liu Siyang, Gao Han, et al.Waterline Detection Method for Unmanned Surfaced Vessel Based on Improved Hough Transform[J]. Science technology and Engineering, 2018, 18(31): 36-41.
[15] 曾文静, 万磊, 张铁栋, 等. 基于海面可见光图像的海界线快速检测[J]. 光学学报, 2012(1): 90-97.
Zeng Wenjing, Wan Lei, Zhang Tiedong, et al.Fast Detection of Sea Line Based on the Visible Characteristics of Marine Images[J]. Acta Optica Sinica, 2012(1): 90-97.
[16] 黄英东, 范宁军, 李杰. 一种基于海天线检测的舰船定位方法[J]. 弹箭与制导学报, 2008(5): 286-288.
Huang Yingdong, Fan Ningjun, Li Jie.A Method of Ship Position Based on Sea-sky-line Detection[J]. Journal of Projectiles, Rockets, Missiles and Guidance, 2008(5): 286-288.
[17] 徐良玉, 马录坤, 谢燮, 等. 基于结构森林边缘检测和Hough变换的海天线检测[J]. 上海大学学报(自然科学版), 2017, 23(1): 47-55.
Xu Liangyu, Ma Lukun, Xie Xie, et al.Sea-sky Line Detection based on Structured Forests Edge Detection and Hough Transform[J]. Journal of Shanghai University (Natural Science Edition), 2017, 23(1): 47-55.
[18] 张志祥, 王秋萍, 朱旭芳, 等. 基于场景划分的海天线检测方法[J]. 华中科技大学学报(自然科学版), 2020, 48(8): 61-66.
Zhang Zhixiang, Wang Qiuping, Zhu Xufang, et al.Sea-sky Line Detection Method based on Scene Division[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2020, 48(8): 61-66.
[19] 雷琴, 施朝健, 陈婷婷. 基于均值漂移边缘提取的海天线检测方法[J]. 计算机应用与软件, 2015, 32(6): 208-211.
Lei Qin, Shi Chaojian, Chen Tingting.Sea-sky Line Detection based on Mean Shift Edge Extraction[J]. Computer Applications and Software, 2015, 32(6): 208-211.
[20] Lin L.A Saliency Gradient Feature Based Sea-Sky-Line Detection Algorithm for USVs Navigation[C]// 2018 Chinese Automation Congress (CAC). Piscataway, NJ: IEEE, 2018: 3503-3506.
[21] Wei Y, Zhang Y.Effective Waterline Detection of Unmanned Surface Vehicles Based on Optical Images[J]. Sensors (S1424-8220), 2016, 16(10): 1-18.
[22] Wang X, Zhang T, Yan L, et al.A Method of Sea-sky-line Detection in Complex Sea Background[C]// MIPPR 2009: Automatic Target Recognition and Image Analysis. International Society for Optics and Photonics. USA: SPIE, 2009: 7495.
[23] Ma T, Ma J, Fu W.Sea-sky Line Extraction with Linear Fitting Based on Line Segment Detection[C]// 2016 9th International Symposium on Computational Intelligence and Design (ISCID). Piscataway, NJ: IEEE, 2016: 46-49.
[24] VonGioi R G, Jakubowicz J, Morel J M, et al. LSD: A Line Segment Detector[J]. Image Processing on Line (S2105-1232), 2012(2): 35-55.
[25] 刘松涛, 沈同圣, 韩艳丽, 等. 舰船目标海天线提取方法研究[J]. 激光与红外, 2003, 17(1): 51-53.
Liu Songtao, Shen Tongsheng, Han Yanli, et al.Research on Locating the Horizontal Region of Ship Target[J]. Laser and infrared, 2003, 17(1): 51-53.
[26] 刘靖逸, 陈金波, 王志恒, 等. 基于超像素分类的海天线检测方法[J]. 计量与测试技术, 2017, 44(3): 104-106, 109.
Liu Jingyi, Chen Jinbo, Wang Zhiheng, et al.Sea-Sky Line Detection Based on Superpixel Classification[J]. Metrology & Measurement Technique, 2017, 44(3): 104-106, 109.
[27] 黄明晶, 王雪梅, 蹇渊, 等. 基于海天线提取和混合灰度差的船舶检测方法[J]. 红外, 2021, 42(6): 29-33.
Huang Mingjing, Wang Xuemei, Jian Yuan, et al.Ship Detection Method Based on Sea-Sky Line Extraction and Mixed Gray Difference[J]. Infrared, 2021, 42(6): 29-33.
[28] Lu J W, Ren J C, Lu Y, et al.A Modified Canny Algorithm for Detecting Sky-sea Line in Infrared Images[C]// Sixth International Conference on Intelligent Systems Design and Applications. Piscataway, NJ: IEEE, 2006: 289-294.
[29] Sheng Y, Yang X, Mcreynold D.Real-world Multisensor Image Alignment Using Edge Focusing and Hausdorf Distances[C]// Spie Aerosense: Sensor Fusion- architectures, Algorithms & Applications III. Colorado: SPIE, l999: l74-l84.
[30] Randrianarisoa V, Yang X, Sheng Y, et al.Robust Automatic Ground Image Feature Extraction for Multisensor Image Registration[C]// Proc. SPIE. San Diego: SPIE, 2001: 4385.
[31] 刘士建, 蒋敏, 庄良. 一种快速有效的红外图像中海天线提取算法[J]. 红外技术, 2011, 33(4): 230-232, 240.
Liu Shijian, Jiang Min, Zhuang Liang.A Fast and Effective Algorithm for Sea-sky-line Extraction in Infrared Images[J]. Infrared Technology, 2011, 33(4): 230-232, 240.
[32] Boroujeni N, Etemad S, Ali W.Robust Horizon Detection Using Segmentation for UAV Applications[C]// 2012 9th Conference on Computer and Robot Vision. Toronto, Ontario, Canada: IEEE, 2012: 28-30.
[33] Tang D, Sun G, Wang D, et al.Research on Infrared ship Detection Method in Sea-Sky Background[C]// International Symposium on Photoelectronic Detection and Imaging 2013: Infrared Imaging and Applications. USA: SPIE, 2013: 8907.
[34] 王丁禾, 牛照东, 张聘义, 等. 利用模糊综合评判技术提取红外图像海天线[J]. 光电工程, 2012, 39(11): 67-74.
Wang Dinghe, Niu Zhaodong, Zhang Pingyi, et al.Sea-sky-line Extraction in Infrared Images Using Fuzzy Synthetical Judgment[J]. Optoelectronic Engineering, 2012, 39(11): 67-74.
[35] Zou R B, Shi C C. A Sea-Sky Line Identification Algorithem Based on Shearlets for Infrared Image[J]. Advanced Materials Research (S1662-8985), 2013, 846/847: 1031-1035.
[36] Park J H, Nam K G, Joo J H.A Partially Occluded Sea-Sky Line Detection Algorithm[C]// 2011 International Conference on Image Processing, Computer Vision, & Pattern Recognition. Las Vegas NV: CSREA Press, 2011: 762-766.
[37] Lin L.A Saliency Gradient Feature Based Sea-Sky-Line Detection Algorithm for USVs Navigation[C]// 2018 Chinese Automation Congress (CAC). Piscataway, NJ: IEEE, 2018: 3503-3506.
[38] 王杰. 无人水面艇的自主航行避障系统设计与实现[D].南京: 南京信息工程大学, 2020.
Wang Jie.Design and Implementation of Autonomous Navigation Obstacle Avoidance System for Unmanned Surface Vehicle[D]. Nanjing: Nanjing University of Information Engineering, 2020.
[39] Lee J H, Lee S, Zhang G, et al.Outdoor Place Recognition in Urban Environments Using Straight Lines[C]// 2014 IEEE International Conference on Robotics and Automation (ICRA). Piscataway, NJ: IEEE, 2014: 5550-5557.
[40] Akinlar C, Topal C.EDLines: A Real-time Line Segment Detector with a False Detection Control[J]. Pattern Recognition Letters (S0167-8655), 2011, 32(13): 1633-1642.
[41] Itti L, Koch C, Niebur E.A Model of Saliency-based Visual Attention for Rapid Scene Analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence (S0162-8828), 1998, 20(11): 1254-1259.
[42] 叶玲利. 内河视频监控中运动船舶检测和遮挡分离算法研究[D]. 武汉: 武汉理工大学, 2013.
Ye Lingli.Research on Moving Ship Detection and Occlusion Separation Algorithm in Inland Video Surveillance[D]. Wuhan: Wuhan University of Technology, 2013.
[43] Chapel M N, Bouwmans T.Moving Objects Detection with a Moving Camera: A Comprehensive Review[J]. Computer Science Review (S1574-0137), 2020, 38: 100310.
[44] Gu D, Jin X.A Review on Intrusion Detection of Moving Objects at Sea[J]. Journal of Image and Signal Processing (S2325-6745), 2020, 9(3): 129-136.
[45] Jain R, Nagel H H.On the Analysis of Accumulative Difference Pictures from Image Sequences of Real World Scenes[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence (S0162-8828), 1979, 1(2): 206-214.
[46] Voles P, Teal M, Sanderson J.Target Identification in a Complex Maritime Scene[C]// Colloquium on Motion Analysis and Tracking. Piscataway, NJ: IEEE, 1999: 15.
[47] Zhu Q Y, Jiang Y L, Chen B.Design and Implementation of Video-based Detection System for Wharf Ship[C]// IET International Conference on Smart and Sustainable City 2013 (ICSSC 2013). Shanghai, China: IET, 2013: 493-496.
[48] Yang T, Zhang S, Zhou G, et al.Design of a Real-time System of Moving Ship Tracking on-board Based on FPGA in Remote Sensing Images[C]// International Conference on Intelligent Earth Observing and Applications 2015. USA: International Society for Optics and Photonics, 2015.
[49] Zhou S, Hong S, Guo Q, et al.A Fusion Detection Algorithm of Motional Ship in Bridge Collision Avoidance System[C]// 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE). Los Alamitos, CA: IEEE Computer Society, 2017: 1-5.
[50] Xie L, Chen J, Yan Z, et al.A New Method of Inland River Overloaded Ship Identification Using Digital Image Processing[J]. Lecture Notes in Electrical Engineering (S1876-1100), 2013, 220: 499-506.
[51] Arshad N, Moon K S, Kim J N.An Adaptive Moving Ship Detection and Tracking Based on Edge Information and Morphological Perations[C]// International Conference on Graphic and Image Processing (ICGIP 2011). USA: International Society for Optics and Photonics, 2011: 8285.
[52] 穆孟超, 胡钊政, 李祎承. 基于虚拟线圈的内河船舶流量检测方法[J]. 交通信息与安全, 2017, 35(4): 44-51.
Mu Mengchao, Hu Zhaozheng, Li Yicheng.A Method for Detection of Ship Traffic in Inland Waterways Based on Virtual Loop[J]. Journal of Transport Information and Safety, 2017, 35(4): 44-51.
[53] 韩超, 邓甲昊, 邹金慧, 等. 基于差分均值背景提取和矩阵分区目标检测算法的研究[J]. 北京理工大学学报, 2012, 32(12): 1247-1251, 1257.
Han Chao, Deng Jiahao, Zou Jinhui, et al.Background Extraction Based on Differential Mean Method and Shadow Detection Using Matrix Subregion Partition[J]. Transactions of Beijing Institute of Technology, 2012, 32(12): 1247-1251, 1257.
[54] Hu W C, Yang C Y, Huang D Y.Robust Real-time Ship Detection and Tracking for Visual Surveillance of Cage Aquaculture[J]. Journal of Visual Communication and Image Representation (S1047-3203), 2011, 22(6): 543-556.
[55] 夏海英, 何利平, 黄思奇. 基于时空分布的混合高斯背景建模改进方法[J]. 计算机应用研究, 2015, 32(5): 1546-1548, 1553.
Xia Haiying, He Liping, Huang Siqi.Modified GMM Algorithm Based on Spatiotemporal Distribution[J]. Application Research of Computers, 2015, 32(5): 1546-1548, 1553.
[56] Choi J M, Chang H J, Yoo Y J, et al.Robust Moving Object Detection Against Fast Illumination Change[J]. Computer Vision & Image Understanding (S1077-3142), 2012, 116(2): 179-193.
[57] Shah M, Deng J D, Woodford B J.A Self-adaptive Codebook (SACB) Model for Real-time Background Subtraction[J]. Image &Vision Computing (S0262-8856), 2015, 38: 52-64.
[58] Barnich O, Droouenbroeck M V.Vibe: a Universal Background Subtraction Algorithm for Video Sequences[J]. IEEE Transactions on Image Proceeding (S1057-7149), 2011, 20(6): 1709-1724.
[59] Elgammal A, Harwood D, Davis L.Non-parametric Model for Background Subtraction[C]// European Conference on Computer Vision. Heidelberg: Springer, Berlin, 2000, 18(43): 751-767.
[60] Wulff J, Sevilla-Lara L, Black M J.Optical Flow in Mostly Rigid Scenes[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE Computer Society, 2017: 6911-6920.
[61] Li H, Man Y.Moving Ship Detection Based on Visual Saliency for Video Satellite[C]// 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Piscataway, NJ: IEEE, 2016: 1248-1250.
[62] 张可, 杨灿坤, 周春平, 等. 无人机视频图像运动目标检测算法综述[J]. 液晶与显示, 2019, 34(1): 98-109.
Zhang Ke, Yang Cankun, Zhou Chunping, et al.Review of Moving Target Detection Algorithms for UAV Video Images[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(1): 98-109.
[63] Armangué X, Araújo H, Salvi J.A Review on Egomotion by Means of Differential Epipolar Geometry Applied to the Movement of a Mobile Robot[J]. Pattern Recognition (S0031-3203), 2003, 36(12): 2927-2944.
[64] Deng C, Cao Z, Fang Z, et al.Ship Detection from Optical Satellite Image Using Optical Flow and Saliency[C]// MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications. USA: International Society for Optics and Photonics, 2013: 8921.
[65] 李成美, 白宏阳, 郭宏伟. 一种改进光流法的运动目标检测及跟踪算法[J]. 仪器仪表学报, 2018, 39(5): 249-256.
Li Chengmei, Bai Hongyang, Guo Hongwei.Moving Object Detection and Tracking Based on Improved Optical Flow Method[J]. Chinese Journal of Scientific Instrument, 2018, 39(5): 249-256.
[66] 杨依忠, 张强, 汪鹏飞. 基于改进ViBe算法与三帧差法的运动检测算法[J]. 合肥工业大学学报(自然科学版), 2018, 41(8): 1052-1058.
Yang Yizhong, Zhang Qiang, Wang Pengfei.A Novel Moving Objects Detection Algorithm Based on Improved ViBe and Three-frame Differencing Algorithm[J]. Journal of Hefei University of Technology(Natural Science), 2018, 41(8): 1052-1058.
[67] Bloisi D, Iocchi L, Leone G R, et al.A Distributed Vision System for Boat Traffic Monitoring in the Venice Grand Canal[J]. International Journal of Pattern Recognition and Artificial Intelligence (S0218-0014), 2009, 3(7): 1477-1502.
[68] 李柯, 黄席樾, 刘俊, 等. 桥梁防撞系统中的红外船舶目标检测算法[J]. 红外技术, 2008, 30(6): 311-315.
Li Ke, Huang Xiyue, Liu Jun, et al.Infrared Ship Target Detection Algorithm in Bridge Anti-collision System[J]. Infrared Technology, 2008, 30(6): 311-315.
[69] Agrawal A P, Tyagi N.Review on Digital Image Segmentation Techniques[J]. Journal of Critical Reviews (S2394-5125), 2020, 7(3): 779-784.
[70] Puja S, Suchithra R.Review Study on Digital Image Processing and Segmentation[J]. American Journal of Computer Science and Technology (S2582-158X), 2019, 2(4): 68-72.
[71] 谢凤英, 赵丹培, 李露, 等. 数字图像处理及应用[M].北京: 电子工业出版社, 2016.
Xie Fengying, Zhao Danpei, Li Lu, et al.Digital Image Processing and Application[M]. Beijing: Electronic Industry Press, 2016.
[72] 黄鹏, 郑淇, 梁超. 图像分割方法综述[J]. 武汉大学学报(理学版), 2020, 66(6): 519-531.
Huang Peng, Zheng Qi, Liang Chao.Overview of Image Segmentation Methods[J]. Journal of Wuhan University(Natural Science Edition), 2020, 66(6): 519-531.
[73] 严忠贞. 内河在航船舶动态跟踪和航迹融合方法研究[D]. 武汉: 武汉理工大学, 2013.
Yan Zhongzhen.Research on Inland River Shipping Tracking and Track Fusion Method[D]. Wuhan: Wuhan University of Technology, 2013.
[74] 刘进, 杨洁, 庞瑞帆, 等. 一种基于多尺度特征簇的舰船目标快速定位与识别方法[J]. 武汉大学学报·信息科学版, 2016, 41(1): 111-116.
Liu Jin, Yang Jie, Pang Ruifan, et al.A Method for Fast Ship Detection and Recognition in Sea-Sky Background Based on Multrscale Feature Cluster[J]. Geomatics and Information Science of Wuhan University, 2016, 41(1): 111-116.
[75] 张静. 基于图像的船舶目标检测[D]. 南京: 南京理工大学, 2007.
Zhang Jing.Ship Target Detection based on Image[D]. Nanjing: Nanjing University of Science and Technology, 2007.
[76] Pare S, Kumar A, Singh G K, et al.Image Segmentation Using Multilevel Thresholding: a Research Review[J]. Iranian Journal of Science and Technology, Transactions of Electrical Engineering (S2228-6179), 2020, 44(1): 1-29.
[77] Smith A A W, Teal M K, Voles P. The Statistical Characterization of the Sea for the Segmentation of Maritime Images[C]// Video/Image Processing and Multimedia Communications, 2003. 4th EURASIP Conference Focused on. Piscataway, NJ: IEEE, 2003: 489-494.
[78] 朱城和. 基于图像序列水上运动船舶识别跟踪技术研究[D]. 赣州: 江西理工大学, 2013.
Zhu Chenghe.Research on Recognition and Tracking Technology of Water Moving Ships Based on Image Sequence[D]. Ganzhou: Jiangxi University of Science and Technology, 2013.
[79] 万磊, 曾文静, 秦再白, 等. 海面目标自适应实时检测[J]. 上海交通大学学报, 2012, 46(9): 1421-1427.
Wan Lei, Zeng Wenjing, Qin Zaibai, et al.Real-time Detection of Sea Surface Targets[J]. Journal of Shanghai Jiaotong University, 2012, 46(9): 1421-1427.
[80] Bezdek J C.Pattern Recognition with Fuzzy Objective Function Algorithms[M]. New York: Plenum Press, 1981.
[81] 徐小来, 房晓丽. 基于改进的直觉模糊核聚类的图像分割方法[J]. 计算机工程与应用, 2019, 55(17): 227-231.
Xu Xiaolai, Fang Xiaoli.Image Segmentation Based on Intuitionistic Fuzzy Kernel c-Means Clustering Algorithms[J]. Computer Engineering and Application, 2019, 55(17): 227-231.
[82] 黄永林, 叶玉堂, 乔闹生, 等. 基于快速模糊C均值聚类算法的红外图像分割[J]. 强激光与粒子束, 2011, 23(6): 1467-1470.
Huang Yonglin, Ye Yutang, Qiao Naosheng, et al.Infrared Image Segmentation Based on Fast Fuzzy C-means Clustering[J]. High Power Laser and Particle Beams, 2011, 23(6): 1467-1470.
[83] Saman S.Binary Image Segmentation Using Classification Methods: Support Vector Machines, Artificial Neural Networks and Kth Nearest Neighbours[J]. International Journal of Computer (IJC)(S0951-192X), 2017, 24(1): 56-79.
[84] Han X H, Xiong X, Duan F.A New Method for Image Segmentation Based on BP Neural Network and Gravitational Search Algorithm Enhanced by Cat Chaotic Mapping[J]. Applied Intelligence (S1573-7497), 2015, 43(4): 855-873.
[85] Li W, Zou B, Zhang L.Ship Detection in a Large Scene SAR Image Using Image Uniformity Description Factor[C]// 2017 SAR in Big Data Era: Models, Methods and Applications (BIGSARDATA). Piscataway, NJ: IEEE, 2017: 1-5.
[86] 卢运娇, 罗莎. 纹理高阶分形特征的海面舰船目标检测方法[J]. 舰船科学技术, 2020, 42(4): 37-39.
Lu Yunjiao, Luo Sha.Research on Detection Method of Ship Target on Sea Surface Based on High-order Fractal Texture[J]. Ship Science and Technology, 2020, 42(4): 37-39.
[87] 周东, 李昀峰, 高妍, 等. 融合区域纹理梯度的船舶阴影视频去除算法[J]. 北京邮电大学学报, 2017, 40(增1): 81-84, 97.
Zhou Dong, Li Yunfeng, Gao Yan, et al.Ship Shadow Elimination Algorithm Based on HSV and Texture of Gradient Amendment[J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(S1): 81-84, 97.
[88] 王焜. 基于视频的运动船只识别与跟踪技术研究[D]. 厦门: 厦门大学, 2009.
Wang Kun.Maritime Objects Recognition and Tracking Technology based on Video[D]. Xiamen: Xiamen University, 2009.
[89] 汪闽, 骆剑承, 明冬萍. 高分辨率遥感影像上基于形状特征的船舶提取[J]. 武汉大学学报·信息科学版, 2005, 30(8): 685-688.
Wang Min, Luo Jiancheng, Ming Dongping.Extract Ship Targets from High Spatial Resolution Remote Sensed Imagery with Shape Feature[J]. Geomatics and Information Science of Wuhan University, 2005, 30(8): 685-688.
[90] Hill R A, Thomason A G.Mapping Wood Land Species Composition and Structure Using Airbone Spectral and LIDAR Data[J]. International Journal of Remote Sensing (S0143-1161), 2005, 26(4): 3763-3779.
[91] 沈睿. 基于VTS图像的船舶自动跟踪技术与实现[D]. 大连: 大连海事大学, 2010.
Shen Rui.The Ship Automatic Tracking Technique and Implementation Based on VTS Images[D]. Dalian: Dalian Maritime University, 2010.
[92] 董建国. 基于形状的图像目标识别方法研究[D]. 西安: 西安电子科技大学, 2009.
Dong Jianguo.Research on Image Target Recognition Method Based on Shape[D]. Xi'an: Xidian University, 2009.
[93] 颜利斌. 基于机器视觉的内河航道船艇特征提取与识别[D]. 长沙: 湖南师范大学, 2015.
Yan Libin.Feature Extraction and Recognition of Inland Waterway Vessels Based on Machine Vision[D]. Changsha: Hunan Normal University, 2015.
[94] 杨高星. 基于计算机视觉的船舶跟踪及类型识别研究[D]. 大连: 大连海事大学, 2017.
Yang Gaoxing.Research of Ship Tracking and Type Recognition Based on Computer Vision[D]. Dalian: Dalian Maritime University, 2017.
[95] 安婷. 基于特征信息提取的船舶检测与跟踪方法研究[D]. 北京: 华北电力大学, 2015.
An Ting.Research on Ship Detection and Tracking Method Based on Extraction of Feature Information[D]. Beijing: North China Electric Power University, 2015.
[96] Urbanowicz R J, Moore J H, Ritchie M.Learning Classifier Systems: A Complete Introduction, Review, and Roadmap[J]. Journal of Artificial Evolution and Applications (S1687-6229), 2009(1): 1-25.
[97] 张丽梅, 乔立山, 陈松灿. 基于张量模式的特征提取及分类器设计综述[J]. 山东大学学报(工学版), 2009, 39(1): 6-14, 40.
Zhang Limei, Qiao Lishan, Chen Songcan.A Survey of Feature Extraction and Classifier Design Based on Tensor Pattern[J]. Journal of Shandong University (Engineering Science), 2009, 39(1): 6-14, 40.
[98] 张迪飞, 张金锁, 姚克明, 等. 基于SVM分类的红外舰船目标识别[J]. 红外与激光工程, 2016, 45(1): 179-184.
Zhang Difei, Zhang Jinsuo, Yao Keming, et al.Infrared Ship-target Recognition Based on SVM Classification[J]. Infrared and Laser Engineering, 2016, 45(1): 179-184.
[99] 梁锦雄, 王刻奇. 基于BP神经网络的船舰目标识别分类[J]. 舰船科学技术, 2015, 37(3): 206-209.
Liang Jinxiong, Wang Keqi.Ship Recognition Based on BP Network[J]. Ship Science and Technology, 2015, 37(3): 206-209.
[100] 李开明, 张群, 罗迎, 等. 地面车辆目标识别研究综述[J]. 电子学报, 2014, 42(3): 538-546.
Li Kaiming, Zhang Qun, Luo Ying, et al.Review of Ground Vehicles Recognition[J]. Acta Electronica Sinica, 2014, 42(3): 538-546.
[101] Bousetouane F, Morris B.Fast CNN Surveillance Pipeline for Fine-grained Vessel Classification and Detection in Maritime Scenarios[C]// 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). Piscataway, NJ: IEEE, 2016: 242-248.
[102] 顾佼佼, 李炳臻, 刘克, 等. 基于改进Faster R-CNN的红外舰船目标检测算法[J]. 红外技术, 2021, 43(2): 170-178.
Gu Jiaojiao, Li Bingzhen, Liu Ke, et al.Infrared Ship Target Detection Algorithm Based on Improved Faster R-CNN[J]. Infrared Technology, 2021, 43(2): 170-178.
[103] 霍煜豪, 徐志京. 基于改进RA-CNN的舰船光电目标识别方法[J]. 上海海事大学学报, 2019, 40(3): 38-43.
Huo Yuhao, Xu Zhijing.Photoelectric Ship Target Identification Method Based on Improved RA-CNN[J]. Journal of Shanghai Maritime University, 2019, 40(3): 38-43.
[104] 于洋, 李世杰, 陈亮, 等. 基于改进YOLO v2的船舶目标检测方法[J]. 计算机科学, 2019, 46(8): 332-336.
Yu Yang, Li Shijie, Chen Liang, et al.Ship Target Detection Based on Improved Yolo V2[J]. Computer Science, 2019, 46(8): 332-336.
[105] 姜文志, 李炳臻, 顾佼佼, 等. 基于改进YOLO V3的舰船目标检测算法[J]. 电光与控制, 2021, 28(6): 52-56, 67.
Jiang Wenzhi, Li Bingzhen, Gu Jiaojiao, et al.A Ship Target Detection Algorithm Based on Improved POLO V3[J]. Electronics Optics & Control, 2021, 28(6): 52-56, 67.
[106] 赵蓬辉, 孟春宁, 常胜江. 基于改进VGG网络的单阶段船舶检测算法[J]. 光电子·激光, 2019, 30(7): 719-730.
Zhao Penghui, Meng Chunning, Chang Shengjiang.Single Stage Ship Detection Algorithm Based on Improved VGG Network[J]. Journal of Optoelectronics·Laser, 2019, 30(7): 719-730.