A Target Identification Technique for Unmanned Surface Vessel Based on Deep Learning

doi:10.12382/bgxb.2022.B021

Abstract

Abstract: At present, China is vigorously developing marine weapons and equipment, and the research on unmanned weapons and equipment has received extensive attention. The intelligentization of unmanned surface vessels is a research hotspot. To meet the detection and high-precision positioning requirements of large and medium-sized targets, this paper focuses on the design of target identification technique for unmanned surface vessel based on deep learning. Firstly, the multi-source and multi-system collaborative sensing architecture design is used to solve the problems of equipment intelligent computing task duplication and resource waste as well as deep learning acceleration. Secondly, multi-level feature extraction, analysis and fusion technique is designed to determine the features that should be selected for single/multi-sensors. Finally, the selected features are used to design multi-feature target detection and identification methods based on deep learning, and a multi-source multi-dimensional joint detection and identification processing method based on deep learning networks is established. The experimental results show that the recognition rate exceeds 99.7% for visual images, indicating that this technique has good recognition effects.

Key words: unmannedsurfacevessel, targetidentification, featureextraction, deeplearning

CLC Number:

TH98
TP391.4

WANG Liang, CHEN Jianhua, LI Ye. A Target Identification Technique for Unmanned Surface Vessel Based on Deep Learning[J]. Acta Armamentarii, 2022, 43(S2): 13-19.

References

［1］王克义, 刘天生, 吴爱燕.人工智能技术在武器装备中的应用研究［J］.四川兵工学报, 2006, 27(1):13-16.
WANG K Y,LIU T S,WU A Y. Research on the application of artificial intelligence technology in weapon equipment［J］.Journal of Sichuan Ordnance, 2006, 27(1):13-16. (in Chinese)
［2］韩晔彤.人工智能技术发展及应用研究综述［J］. 电子制作, 2016(6X):95.
HAN Y T.Review on the development and application of artificial intelligence technology［J］.Practical Electronics, 2016(6X):95.(in Chinese)
［3］李家良.水面无人艇发展与应用［J］.火力与指挥控制, 2012, 37(6):203-207.
LI J L.Development and appl ication of unmanned surface vehicle［J］. Fire Control & Command Control, 2012, 37(6):203-207. (in Chinese)
［4］张京晶,万宇婷.测量无人艇研究进展综述［J］. 海洋测绘, 2020, 40(4):39-43.
ZHANG J J,WAN Y T. Summary on USV based on maritime measurement［J］.Hydrographic Surveying and Charting, 2020, 40(4): 39-43. (in Chinese)
［5］张卫东, 刘笑成, 韩鹏.水上无人系统研究进展及其面临的挑战［J］.自动化学报, 2020, 46(5): 847-857.
ZHANG W D,LIU X C,HAN P.Progress and challenges of overwater unmanned systems［J］. Acta Automatica Sinica, 2020, 46(5): 847-857. (in Chinese)
［6］马忠丽, 文杰, 梁秀梅,等. 无人艇视觉系统多类水面目标特征提取与识别［J］. 西安交通大学学报, 2014, 48(8):60-66.
MA Z L,WEN J,LIANG X M,et al.Extraction and recognition of features from multi-types of surface targets for visual systems in unmanned surface vehicl［J］.Journal of Xi'an Jiaotong University, 2014, 48(8):60-66. (in Chinese)
［7］程亮, 杨渊, 张云飞,等.面向无人艇智能感知的水上目标识别算法研究［J］.电子测量与仪器学报, 2021,35(9): 99-104.
CHENG L,YANG Y,ZHANG Y F,et al.Research on water target recognition algorithm for unmanned surface vessel［J］. Journal of Electronic Measurement and Instrumentation, 2021,35(9):99-104. (in Chinese)
［8］张志学.无人艇的可见光视觉目标跟踪系统［J］. 舰船科学技术, 2022,44(13):153-156.
ZHANG Z X.Design of visible light visual target tracking system for USV［J］.Ship Science and Technology［J］.Ship Science and Technology, 2022,44(13):153-156. (in Chinese)
［9］ VIOLA P A, JONES M J. Robust real-time object detection［J］.International Journal of Computer Vision∥Proceedings of the 2nd International Workshop on Statistical and Computational Theories of Vision-Modeling, Learning, Computing, and Sampling. 2001:87.
［10］ WALTHER D B, EDGINTON D, KOCH C. Detection and tracking of objects in underwater video［C］∥Proceedings of Computer Vision and Pattern Recognition.Washington，D.C.,US:IEEE, 2004,1:544-549.DOI:10.1109/CVPR.2004.80.
［11］ TIAN S, WANG C, ZHANG H, et al.A wavelet based targets detection method for high resolution airborne SAR data［C］∥Proceedings of Barcelona.Spain:IEEE,2007:2071-2073.DOI:10.1109/IGARSS.2007.4423240.
［12］ GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation［C］∥Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus,OH,US:IEEE,2014. DOI: 10.1109/CVPR.2014.81.
［13］ REN S Q, HE K M, GIRSHICK R B, et al.Faster R-CNN: towards real-time object detection with region proposal networks［J］.IEEE Transactions on Pattern Analysis & Machine Intelligence,2015,39(6):1137-1149.10.1109/TPAMI.2016.2577031.
［14］ HE K M, GKIOXARI G, DOLLáR P, et al.Mask r-CNN［C］∥Proceedings of the IEEE International Conference on Computer Vision.Venice, Italy:IEEE,2017:2961-2969.
［15］ REDMON J, FARHADI A.Yolov3:an incremental improvement［J］.arXiv Preprint arXiv:1804.02767, 2018.
［16］ ZHAO Q J, SHENG T, WANG Y T, et al. M2det: a single-shot
object detector based on multi-level feature pyramid network［C］∥Proceedings of the AAAI Conference on Artificial Intelligence， 2019, 33: 9259-9266.
［17］ ZHOU Y, TAO H.A background layer model for object tracking through occlusion［C］∥Proceedings of IEEE International Conference on Computer Vision, 2003. Nice, France：IEEE, 2003，2:1079-1085. DOI: 10.1109/ICCV.2003.1238469.
［18］李闯,丁晓青,吴佑寿.一种改进的 AdaBoost 算法——AD AdaBoost［J］.计算机学报,2007, 30(1): 103-109.
LI C,DING X Q,WU Y S.A revised ada boost algorithm—AD ada boost［J］.Chinese Journal of Computers, 2007, 30(1): 103-109. (in Chinese)
［19］ DU B, ZHANG L, TAO D, et al. Unsupervised transfer learning for target detection from hyperspectral images［J］. Neurocomputing, 2013, 120(10):72-82.
［20］ DU B, ZHANG L P. Target detection based on a dynamic subspace［J］. Pattern Recognition, 2014, 47(1):344-358.
［21］冯冬竹, 范琳琳, 余航,等.自适应轮廓的变分水平集复杂背景多目标检测［J］.软件学报, 2017, 28(10):2797-2810.
FENG D Z,FAN L L,YU H,et al.Adaptive contour based variational level set model for multiple target detection in complex background［J］.Journal of Software, 2017, 28(10):2797-2810.(in Chinese)
［22］丁鹏, 张叶, 贾平, 等.基于视觉显著性的海面舰船检测技术［J］.电子学报, 2018, 46(1): 127-134.
DING P,ZHANG Y,JIA P,et al.Ship detection on sea surface based on visual saliency［J］.Acta Electronica Sinica, 2018, 46(1): 127-134.(in Chinese)
［23］裴伟, 许晏铭, 朱永英, 等.改进的SSD航拍目标检测方法［J］.软件学报,2019, 30(3):248-268.
PEI W,XU Y M,ZHU Y Y,et al. The target detection method of aerial photography images with improved SSD［J］.Journal of Software, 2019, 30(3):248-268. (in Chinese)

[1]	LI Liangfu， CHEN Weidong， GAO Qiang， XU Kailuan， LIU Xuan， HE Xi， QIAN Jun. Deep Learning-based Intelligent Target Recognition Technology for Electro-optical System [J]. Acta Armamentarii, 2022, 43(S1): 162-168.
[2]	LI Jinqing, LIU Zefei, MAN Zhenlong. Key Generation Method Based on Generative Adversarial Network and Its Application in Low-light-level Image Encryption [J]. Acta Armamentarii, 2022, 43(2): 337-344.
[3]	YANG Chuandong， QIAN Lizhi， XUE Song, CHEN Dong, LING Chong. Review on Target Detection of Image Homing Ammunition [J]. Acta Armamentarii, 2022, 43(10): 2687-2704.
[4]	LI Heng, YE Zukun, ZHA Wenbin, WANG Yulin. Tool Wear Online Monitoring Based on Multi-sensor Information Decision-making Level Fusion [J]. Acta Armamentarii, 2021, 42(9): 2024-2031.
[5]	PU Yunwei， LIU Taotao， GUO Jiang， WU Haixiao. Radar Emitter Signal Recognition Based on Convolutional Neural Network and Coordinate Transformation of AmbiguityFunction Main Ridge [J]. Acta Armamentarii, 2021, 42(8): 1680-1689.
[6]	ZHU Min， XU Aiqiang， XU Qing， LI Ruifeng. Fault Diagnosis of Analog Circuits Based on Improved Multilayer Kernel Extreme Learning Machine [J]. Acta Armamentarii, 2021, 42(2): 356-369.
[7]	ZHANG Feng, ZHANG Chao, YANG Huamin, WANG Fabin. Deep Learning-based Color Compensation Method for Projected Images in Combat Assistant System [J]. Acta Armamentarii, 2021, 42(11): 2418-2423.
[8]	XUE Lingzhi, ZENG Xiangyang, YANG Shuang. Underwater Acoustic Target Recognition Algorithm Based on Generative Adversarial Networks [J]. Acta Armamentarii, 2021, 42(11): 2444-2452.
[9]	HUANG Shaling， ZHU Hongzhi， CHENG Xiangli， XIE Yubin. Adaptive Threshold Setting Method for Layer-count in Penetration Fuse [J]. Acta Armamentarii, 2020, 41(9): 1762-1771.
[10]	WANG Cheng， WU Yan， YANG Tingfei. Improving the Detection Accuracy of Wake Targets by Improved One-class Support Vector Machine [J]. Acta Armamentarii, 2020, 41(9): 1887-1893.
[11]	YU Yue， WANG Honglun. Deep Learning-based Reentry Predictor-corrector Fault-tolerant Guidance for Hypersonic Vehicles [J]. Acta Armamentarii, 2020, 41(4): 656-669.
[12]	YAN Mengda， YANG Rennong， ZUO Jialiang， HU Dongyuan， YUE Longfei， ZHAO Yu. Real-time Computing of Air-to-air Missile Multiple Capture Zones Based on Deep Learning [J]. Acta Armamentarii, 2020, 41(12): 2466-2477.
[13]	LIANG Jie, REN Jun, LI Lei, QI Hang, ZHOU Hongli. Airport Runway Detection Agorithm Based on Accurate Regression of Typical Geometric Shapes [J]. Acta Armamentarii, 2020, 41(10): 2045-2054.
[14]	YU Yongwei， PENG Xi， DU Liuqing， CHEN Tianhao. Real-time Detection of Parts by Assembly Robot Based on Deep Learning Framework [J]. Acta Armamentarii, 2020, 41(10): 2122-2130.
[15]	CHU Shixin， MAO Yunsheng， DONG Zaopeng， YANG Xin. Parameter Identification of High-speed USV Maneuvering Response Model Based on Maximum Likelihood Algorithm [J]. Acta Armamentarii, 2020, 41(1): 127-134.