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A novel target detection method of the unmanned surface vehicle under allweather conditions with an improved yolov3
Li Y(李岩)1,2; Guo JH(郭家宏)1,2,3; Guo XM(郭晓敏)1,2,4; Liu KZ(刘开周)1,2; Zhao WT(赵文涛)1,2,5; Luo YT(罗业腾)1,2; Wang ZY(王振宇)1,2
Department海洋机器人前沿技术中心
Source PublicationSENSORS
ISSN1424-8220
2020
Volume20Issue:17Pages:1-14
Indexed BySCI ; EI
EI Accession number20203509120638
WOS IDWOS:000569776300001
Contribution Rank1
Funding OrganizationLiaoning Provincial Natural Science Foundation of China under Grant 2020-MS-031 ; National Natural Science Foundation of China under Grant 61821005,51809256 ; National Key Research and Development Program of China under Grant No. 2016YFC0300801, 2016YFC0301601, 2016YFC0300604, 2017YFC1405401 ; Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No. XDA13030203 ; Instrument Developing Project of the Chinese Academy of Sciences under Grant No. YZ201441 ; LiaoNing Revitalization Talents Program under Grant No. XLYC1902032 ; China Postdoctoral Science Foundation under Grant No. 2019M662874 ; State Key Laboratory of Robotics at Shenyang Institute of Automation under Grant 2017-Z13
Keywordunmanned surface vehicle real-time object detection deep learning YOLOV3 all-weather condition
Abstract

The USV (unmanned surface vehicle) is playing an important role in many tasks such as marine environmental observation and maritime security, for the advantages of high autonomy and mobility. Detecting the targets on the surface of the water with high precision ensures the subsequent task implementation. However, the changes from the lights and the surface environment influence the performance of the target detecting method in a longterm task with USV. Therefore, this paper proposed a novel target detection method by fusing DenseNet in YOLOV3 to improve the stability of detection to decrease the feature loss, while the target feature is transmitted in the layers of a deep neural network. All the image data used to train and test the proposed method were obtained in the real ocean environment with a USV in the South China Sea during a one month sea trial in November 2019. The experiment results demonstrate the performance of the proposed method is more suitable for the changed weather conditions though comparing with the existing methods, and the realtime performance is available in practical ocean tasks for USV.

Language英语
WOS SubjectChemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS Research AreaChemistry ; Engineering ; Instruments & Instrumentation
Funding ProjectLiaoning Provincial Natural Science Foundation of China[2020-MS-031] ; National Natural Science Foundation of China[61821005] ; National Natural Science Foundation of China[51809256] ; National Key Research and Development Program of China[2016YFC0300801] ; National Key Research and Development Program of China[2016YFC0301601] ; National Key Research and Development Program of China[2016YFC0300604] ; National Key Research and Development Program of China[2017YFC1405401] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA13030203] ; Instrument Developing Project of the Chinese Academy of Sciences[YZ201441] ; LiaoNing Revitalization Talents Program[XLYC1902032] ; China Postdoctoral Science Foundation[2019M662874] ; State Key Laboratory of Robotics at Shenyang Institute of Automation[2017-Z13]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27560
Collection海洋机器人前沿技术中心
Corresponding AuthorLi Y(李岩)
Affiliation1.The State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;
2.Institutes of Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China;
3.University of Chinese Academy of Sciences, Beijing 100049, China;
4.School of Automation and Electrical Engineering, Shenyang Ligong University, Shenyang 110159, China;
5.Shenyang Institute of Automation, Guangzhou, Chinese Academy of Sciences, Guangzhou 511458, China
Recommended Citation
GB/T 7714
Li Y,Guo JH,Guo XM,et al. A novel target detection method of the unmanned surface vehicle under allweather conditions with an improved yolov3[J]. SENSORS,2020,20(17):1-14.
APA Li Y.,Guo JH.,Guo XM.,Liu KZ.,Zhao WT.,...&Wang ZY.(2020).A novel target detection method of the unmanned surface vehicle under allweather conditions with an improved yolov3.SENSORS,20(17),1-14.
MLA Li Y,et al."A novel target detection method of the unmanned surface vehicle under allweather conditions with an improved yolov3".SENSORS 20.17(2020):1-14.
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