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Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

文献类型：期刊论文

作者Qu, Jingxiang1,2; Liu, Ryan Wen1,2; Zhao, Chenjie1,2; Guo, Yu1,2; Xu, Sendren Sheng-Dong3,4; Zhu, Fenghua5; Lv, Yisheng5
刊名IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
出版日期2023-11-07
页码13
ISSN号1524-9050
关键词Waterborne transportation intelligent maritime surveillance vessel draft reading (VDR) multi-task learning image segmentation
DOI10.1109/TITS.2023.3327824
通讯作者Liu, Ryan Wen(wenliu@whut.edu.cn) ; Zhu, Fenghua(fenghua.zhu@ia.ac.cn)
英文摘要The accurate and efficient vessel draft reading (VDR) is an important component of intelligent maritime surveillance, which could be exploited to assist in judging whether the vessel is normally loaded or overloaded. The computer vision technique with an excellent price-to-performance ratio has become a popular medium to estimate vessel draft depth. However, the traditional estimation methods easily suffer from several limitations, such as sensitivity to low-quality images, high computational cost, etc. In this work, we propose a multi-task learning-enabled computational method (termed MTL-VDR) for generating highly reliable VDR. In particular, our MTL-VDR mainly consists of four components, i.e., draft mark detection, draft scale recognition, vessel/water segmentation, and final draft depth estimation. We first construct a benchmark dataset related to draft mark detection and employ a powerful and efficient convolutional neural network to accurately perform the detection task. The multi-task learning method is then proposed for simultaneous draft scale recognition and vessel/water segmentation. To obtain more robust VDR under complex conditions (e.g., damaged and stained scales, etc.), the accurate draft scales are generated by an automatic correction method, which is presented based on the spatial distribution rules of draft scales. Finally, an adaptive computational method is exploited to yield an accurate and robust draft depth. Extensive experiments have been implemented on the realistic dataset to compare our MTL-VDR with state-of-the-art methods. The results have demonstrated its superior performance in terms of accuracy, robustness, and efficiency. The computational speed exceeds 40 FPS, which satisfies the requirements of real-time maritime surveillance to guarantee vessel traffic safety.
资助项目NSFC
WOS研究方向Engineering ; Transportation
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:001124036700001
资助机构NSFC
源URL[http://ir.ia.ac.cn/handle/173211/55015]  
专题多模态人工智能系统全国重点实验室
通讯作者Liu, Ryan Wen; Zhu, Fenghua
作者单位1.Wuhan Univ Technol, Sch Nav, Hubei Key Lab Inland Shipping Technol, Wuhan 430063, Peoples R China
2.State Key Lab Maritime Technol & Safety, Wuhan 430063, Peoples R China
3.Natl Taiwan Univ Sci & Technol, Grad Inst Automat & Control, Taipei 10617, Taiwan
4.Natl Taiwan Univ Sci & Technol, Adv Mfg Res Ctr, Taipei 10617, Taiwan
5.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Qu, Jingxiang,Liu, Ryan Wen,Zhao, Chenjie,et al. Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2023:13.
APA Qu, Jingxiang.,Liu, Ryan Wen.,Zhao, Chenjie.,Guo, Yu.,Xu, Sendren Sheng-Dong.,...&Lv, Yisheng.(2023).Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,13.
MLA Qu, Jingxiang,et al."Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS (2023):13.

入库方式： OAI收割

来源：自动化研究所

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