Real-time Ship Monitoring and Recognition Based on YOLOv3

doi:10.3969/j.issn.1006-2475.2020.03.022

Abstract

Abstract: Ship detection task faces some challenging problems, such as the changeable environment, long-distance small target, poor real-time performance. Compared with other algorithms, the advanced capability of YOLOv3 with backbone network Darknet-53 is analyzed, and a method of real-time ship monitoring and recognition based on YOLOv3 is put forward. Also for some difficult cases, the samples are further trained. So the mean average precision in these difficult cases are improved, and higher robustness is obtained. It is illustrated by the experimental data that the mean average precision of single class is up to 91.82%. The method can work as a support system for ship intelligent driving.

Key words: ship recognition, YOLOv3, further training, ship intelligent driving, real-time monitoring

CLC Number:

TP391.4

QU Wen-yi. Real-time Ship Monitoring and Recognition Based on YOLOv3[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2020.03.022.

References

［1］ KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks［C］// Proceedings of the 25th International Conference on Neural Information Processing Systems. 2012:1097-1105.
［2］ SIMONYAN K, ZISSERMAN A. Very Deep Convolutional Networks for Large-scale Image Recognition ［J/OL］.(2014-09-15)［2019-07-01］. http://arxiv.org/abs/1409.1556v2.
［3］ SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions［C］// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. 2015:1-9.
［4］ GIRSHICK R. Fast R-CNN［C］// Proceedings of IEEE International Conference on Computer Vision. 2015:1440-1448.
［5］ REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017,39(6):1137-1149.
［6］ CHEN X, XIANG S, LIU C L, et al. Vehicle detection in satellite images by hybrid deep convolutional neural networks［J］. IEEE Geoscience and Remote Sensing Letters， 2014,11(10):1797-1801.
［7］ LIU W, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector［C］// European Conference on Computer Vision. 2016:21-37.
［8］ REDMON J, FARHADI A. YOLO9000: Better, faster, stronger［C］// Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. 2017:7263-7271.
［9］胡晨阳. 基于数字图像处理的嵌入式山体滑坡监测系统设计与实现［D］. 成都:西南交通大学, 2017.
［10］李扬. 面向图像目标识别和检测的深度神经网络关键技术研究［D］. 北京:北京邮电大学, 2018.
［11］〖JP3〗AREL I， ROSE D C， KARNOWSKI T P. Deep machine learning: A new frontier in artificial intelligence research［J］. Computational Intelligence Magazine, 2010,5(4):13-18.
［12］HUANG G B， ZHU Q Y， SIEW C K. Extreme learning machine: A new learning scheme of feed forward neural networks［C］// Proceedings of IEEE International Conference on Neural Networks. 2004:985-990.
［13］闫河,王鹏,董莺艳,等. 一种CNN与ELM相结合的船舶分类识别方法［J］. 重庆理工大学学报(自然科学), 2019,33(1):53-57.
［14］PRASAD D K, RAJAN D, RACHMAWATI L, et al. Video processing from electrooptical sensors for object detection and tracking in maritime environment: A survey［J］. IEEE Transactions on Intelligent Transportation Systems, 2017,18(8):1993-2016.
［15］BENJDIRA B, KHURSHEED T, KOUBAA A, et al. Car Detection Using Unmanned Aerial Vehicles: Comparison Between Faster R-CNN and YOLOv3［EB/OL］.(2018-12-28)［2019-07-01］. https://arxiv.org/abs/1812.10968.
［16］REDMON J, DIVVALA S K, GIRSHICK R B, et al. You only look once: Unified, real-time object detection［C］// 2016 IEEE Conference on Computer Vision and Pattern Recognition. 2016:779-788.
［17］HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway. 2016:770.
［18］REDMON J, FARHADI A. YOLOv3: An incremental improvement［C］// IEEE Conference on Computer Vision and Pattern Recognition. 2018:89-95.
［19］温捷文,战荫伟,凌伟林,等. 实时目标检测算法YOLO的批再规范化处理［J］. 计算机应用研究, 2018,35(10):3179-3185.
［20］施辉,陈先桥,杨英.改进YOLO v3的安全帽佩戴检测方法［J］. 计算机工程与应用, 2019,55(11):213-220.
［21］LIN T Y， DOLAR P， GIRSHICK R， et al. Feature pyramid networks for object detection ［C］// 2017 IEEE Conference on Computer Vision and Patern Recognition. 2017:936-944.
［22］LIU W Y, WEN Y D, YU Z D, et al. Large-margin softmax loss for convolutional neural networks［C］// Proceedings of the 33rd International Conference on Machine Learning. 2016:507-516.

[1]	GUO Teng-fei, ZHANG Ze-yan, FU Hong-cai, WANG Ji-xuan, NIU Tian-bao. Lightweight Infrared HV Bushing Identification Algorithm Based on Attention Mechanism [J]. Computer and Modernization, 2022, 0(01): 70-76.
[2]	LI Guo-jin, RONG Yu. Face Mask Detection Algorithm Based on DCN-SERes-YOLOv3 [J]. Computer and Modernization, 2021, 0(09): 12-20.
[3]	LI Long, ZHANG Chong-yang. Vehicle Taillight Detection Method Based on Improved YOLOv3 [J]. Computer and Modernization, 2021, 0(07): 89-94.
[4]	ZHANG Lei, LANG Xian-li, WANG Le. Surface Defect Detection of Aluminum Profile Based on Image Fusion and YOLOv3 [J]. Computer and Modernization, 2020, 0(11): 8-15.
[5]	WANG Chun-hui, WANG Quan-min. Student Specific Behavior Recognition Based on Improved YOLOv3 Network [J]. Computer and Modernization, 2020, 0(07): 90-96.
[6]	QIN Jia, CAO Xue-hong, JIAO Liang-bao. Detection and Tracking of Hard Hat Wearing Based on Deep Learning [J]. Computer and Modernization, 2020, 0(06): 1-.
[7]	CHEN Kai, ZU Li, OU Yi. Face Recognition and Tracking System of Photographic Robot #br# Based on YOLOv3 and ResNet50 [J]. Computer and Modernization, 2020, 0(04): 30-.
[8]	LIU Li-ran 1, CAO Jie2, YANG Lei1, QIU Nan-hao1. An Improved YOLOv3-Tiny Traffic Detection Algorithm [J]. Computer and Modernization, 2020, 0(03): 108-.
[9]	MO Bei-bei, WU Ke-he . Research on Intelligent Detection Technology for Illegal Wearing #br# in Power Operation by Introducing Self-Attention [J]. Computer and Modernization, 2020, 0(02): 115-.
[10]	YANG Lei1, WANG Shao-yun2, LIU Li-ran1, GONG Yong-fu1. A Pedestrian Detection Method in Intelligent Video Monitoring System [J]. Computer and Modernization, 2019, 0(11): 69-.
[11]	ZHANG Xue-yun, MU Yan, ZHANG Jiu-bo. Design of Power Monitoring and Management System Based on WeChat Mini Program [J]. Computer and Modernization, 2017, 0(12): 98-102+107.
[12]	ZHANG Wen, MU Yan, GAO Zhen-xing, LIU Zhi-feng. Application of WebSocket in Real-time Monitoring of Industrial Equipment Data [J]. Computer and Modernization, 2017, 0(11): 111-115.
[13]	GONG An, GAO Yun, GAO Hong-fu. Verify and Check System on Graduate Entrance Examination [J]. Computer and Modernization, 2015, 0(1): 105-108.
[14]	LI Tian-yang. A Heterogeneous Database Access Method of Power Real-time Monitoring System [J]. Computer and Modernization, 2012, 1(201): 74-76,7.
[15]	CUI Wei;ZHONG Hou-jin. Research on Management System of Network in SISG Equipments Overhaul [J]. Computer and Modernization, 2011, 1(8): 91-95.