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Research Review of Ship Draft Observation Methods

Received: 26 February 2023    Accepted: 16 March 2023    Published: 28 March 2023
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Abstract

In order to systematically analyze and summarize the research status and development trend of draft observation, co-work cluster analysis base on draft observation was carried out by using knowledge VOS viewer software. Two research hotspots of draft observation were obtained, human visual observation combined with auxiliary equipment and artificial intelligence observation. The object and method of ship draft observation are summarized, and the main research direction is analyzed. The analysis focuses on improving observation accuracy, reducing labor costs, and being able to apply in complex environments. Future research directions of draft observation mainly include how to combine the traditional ship draft observation with artificial intelligence in the future, reduce error of draft observation to millimeters, identify obstacles, explore neural network autonomous learning optimization models, reduce the observation time and labor cost and meet the requirements of diversified application scenarios. At the same time correct the attitude that the accuracy of the current human observation is not high enough or even a negative in the artificial intelligence research area, finally promote the ship, insurance assessment, maritime and other aspects, in order to make the technology which combined artificial observation with intelligent assistance can be applied in practice to solve relevant lawsuits and provide an authoritative legal basis, and be used as the legal basis in the declaration of the ship and subsequent legal disputes.

Published in American Journal of Traffic and Transportation Engineering (Volume 8, Issue 2)
DOI 10.11648/j.ajtte.20230802.11
Page(s) 33-42
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Ship Draft Observation, Ship Draft Survey, Ship Draft, Draft Observation Accurate, Bulk Carrier, Artificial Intelligence

References
[1] Ni, Xy (2020). Energy conservation A review of the accuracy of ship draft observation for international dry bulk carriers via vessel speed reduction, Ocean engineering. 59. 710-715. DOI 10.1016/j.enpol.2013.04.025.
[2] Jiang Li. (2016). Research on Application of Draft Survey. China Port Science and Technology.
[3] BAOTia n-tian. 2020. Research view of shipping management. Journal of Traffic and Transportation Engineering. 2020 (20). 1671-1637(2020)04-0055-1.
[4] Sun tuna, 2011, Advances in draft survey factor and it’s optimization methods in China, journal of inspection and quarantine. 2011 (21) Van Eck, NJ. (2017). Citation-based clustering of publications using CitNet Explorer and VOSviewer. SCIENTOMETRICS. 11 (2). 1053-1070 DOI. 10.1007/s11192-017-2300-7.
[5] GUO Guang zheng. 2011. Accurate Measurement Techniques and Discussion about Draft. Journal of Qingdao Ocean Shipping Mariners College. 2011 (32).
[6] The International Convention on Load Lines, 1966; LL1966, IMO.
[7] Rules for the Weight Survey of Import and Export Commodities-Weight by Draft (SN/T 0187-93).
[8] Wei Zhan. 2021. The System Research and Implementation for Autorecognition of the Ship Draft via the UAV. International Journal of Antennas and Propagation Volume 2021, Article ID 4617242, 11 pages doi. org/10.1155/2021/4617242.
[9] Hongxiang. He. 2021. Design of Simple Auxiliary Equipment for Ship Draft Observation. Ship Standardization and Quality. 2021 (1). 24-25.
[10] Takahiro Tsujii. 2022. “Automatic draft reading based on image processing,” Opt. Eng. 55 (10), 104104 (2016), doi: 10.1117/1.OE.55.10.104104.
[11] Zhang, G., & Li, J. (2020). Search on recognition method of ship water gauge reading based on improved unet network. Journal of Optoelectronics Laser, 31 (11), 1182-1196.
[12] Wang, B. P., Liu, Z. M., & Wang, H. R. (2021). Computer vision with deep learning for ship draft reading [Article]. Optical Engineering, 60 (2), 10, Article 024105. https://doi.org/10.1117/1
[13] Weihao Li. 2022. Research and Application of U2 NetP Network Incorporating Coordinate Attention for Ship Draft Reading in Complex Situations. Journal of Signal Processing Systems. doi.org/10.1007/s11265-022-01816-w.
[14] Wang lei. 2020. Research on water level recognition method based on deep learning algorithms. Water resource information. 2020 (3) DOI: 10.19364/j.1674-9405.2020.03.009.
[15] Xin Ran. 2011. Draft Line Detection Based on Image Processing for Ship Draft Survey. Proc. of the 2011 2nd International Congress CACS, AISC 145, pp. 39–44.
[16] SHEN Yijun. 2017. Application of ranging technique of radar level meter for draft survey. Chinese journal of ship research. 2017 (6). DOI: 10.3969/j.issn.1673-3185.2017.06.020.
[17] Sivaraman, S. 1990. Field tests prove radar tank gauge accuracy. Oil and Gas Journal, Volume 88, Issue 17, Pages 89-90, Apr 23 1990.
[18] ZHU Jing-lin. 2018. Analysis of Draft Survey Errors on Error Propagation Principle. JOURNAL OF GUANGZHOU MARITIME UNIVERSITY. 2018 (26).
[19] RENATO IVČE, Ph.D. 2011. Determining Weight of Cargo Onboard Ship by Means of Optical Fiber Technology Draft Reading Promat – Traffic Transportation, Vol. 23, 2011.
[20] CHEN Wenwei. 2013. A New Measurement System of Ship Draft. SHIPBUILDING OF CHINA. 2013 (54).
[21] Cui, S.; Pei, X.; Song, H.; Dai, P. Design and Motion Analysis of a Magnetic Climbing Robot Applied to Ship Shell Plate. Machines 2022, 10, 632. doi.10.3390/machines10080632.
[22] Ma Xiaobo. 2016 Design of a Ship Draft Measuring ruler. Ship ocean engineering. 2026. vol45-3.
[23] Li Xinli. 2015 Research on Data Processing Method of Detection for Dynamic Ship Draft Based on Multi-beam Sonar System. The 3rd International Conference on Transportation Information and Safety. June 25 – June 28, 2015, Wuhan, P. R. China.
[24] SUN Guo-yuan. 2002. Study on Automatic Determining Ship’s Draft and Stability Parameters. 2002 (2). NAVIGATION OF CHINA. 1000-4653(2002)02-0028-03.
[25] D. A. ROTHROCK. 2007. The Accuracy of Sea Ice Drafts Measured from U.S. Navy Submarines. JOURNAL OF ATMOSPHERIC AND OCEANIC. TECHNOLOGY. VOLUME 24. DOI: 10.1175/JTECH2097.1.
[26] Zhong Wang. 2020. A Ship Draft Line Detection Method Based on Image Processing and Deep Learning. Journal of Physics: Conference Series. 1575. (2020) 012230 doi: 10.1088/1742-6596/1575/1/012230.
[27] Xing Wang. 2020. On Research of Video Stream Detection Algorithm for Ship Waterline. 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE). DOI 10.1109/ICBAIE49996.2020.00050.
[28] LV Yongxiang, 2017. Status and Analysis of Ship Ultra-draft Detection Technology. Transportation Science & Technology. 2017. (5). DOI 10.3963/j.issn.1671-7570.2017.05.039.
[29] China ship owner mutual insurance association 2022. NO525. lost prevention information.
Cite This Article
  • APA Style

    Yaoming Wei. (2023). Research Review of Ship Draft Observation Methods. American Journal of Traffic and Transportation Engineering, 8(2), 33-42. https://doi.org/10.11648/j.ajtte.20230802.11

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    ACS Style

    Yaoming Wei. Research Review of Ship Draft Observation Methods. Am. J. Traffic Transp. Eng. 2023, 8(2), 33-42. doi: 10.11648/j.ajtte.20230802.11

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    AMA Style

    Yaoming Wei. Research Review of Ship Draft Observation Methods. Am J Traffic Transp Eng. 2023;8(2):33-42. doi: 10.11648/j.ajtte.20230802.11

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  • @article{10.11648/j.ajtte.20230802.11,
      author = {Yaoming Wei},
      title = {Research Review of Ship Draft Observation Methods},
      journal = {American Journal of Traffic and Transportation Engineering},
      volume = {8},
      number = {2},
      pages = {33-42},
      doi = {10.11648/j.ajtte.20230802.11},
      url = {https://doi.org/10.11648/j.ajtte.20230802.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtte.20230802.11},
      abstract = {In order to systematically analyze and summarize the research status and development trend of draft observation, co-work cluster analysis base on draft observation was carried out by using knowledge VOS viewer software. Two research hotspots of draft observation were obtained, human visual observation combined with auxiliary equipment and artificial intelligence observation. The object and method of ship draft observation are summarized, and the main research direction is analyzed. The analysis focuses on improving observation accuracy, reducing labor costs, and being able to apply in complex environments. Future research directions of draft observation mainly include how to combine the traditional ship draft observation with artificial intelligence in the future, reduce error of draft observation to millimeters, identify obstacles, explore neural network autonomous learning optimization models, reduce the observation time and labor cost and meet the requirements of diversified application scenarios. At the same time correct the attitude that the accuracy of the current human observation is not high enough or even a negative in the artificial intelligence research area, finally promote the ship, insurance assessment, maritime and other aspects, in order to make the technology which combined artificial observation with intelligent assistance can be applied in practice to solve relevant lawsuits and provide an authoritative legal basis, and be used as the legal basis in the declaration of the ship and subsequent legal disputes.},
     year = {2023}
    }
    

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  • TY  - JOUR
    T1  - Research Review of Ship Draft Observation Methods
    AU  - Yaoming Wei
    Y1  - 2023/03/28
    PY  - 2023
    N1  - https://doi.org/10.11648/j.ajtte.20230802.11
    DO  - 10.11648/j.ajtte.20230802.11
    T2  - American Journal of Traffic and Transportation Engineering
    JF  - American Journal of Traffic and Transportation Engineering
    JO  - American Journal of Traffic and Transportation Engineering
    SP  - 33
    EP  - 42
    PB  - Science Publishing Group
    SN  - 2578-8604
    UR  - https://doi.org/10.11648/j.ajtte.20230802.11
    AB  - In order to systematically analyze and summarize the research status and development trend of draft observation, co-work cluster analysis base on draft observation was carried out by using knowledge VOS viewer software. Two research hotspots of draft observation were obtained, human visual observation combined with auxiliary equipment and artificial intelligence observation. The object and method of ship draft observation are summarized, and the main research direction is analyzed. The analysis focuses on improving observation accuracy, reducing labor costs, and being able to apply in complex environments. Future research directions of draft observation mainly include how to combine the traditional ship draft observation with artificial intelligence in the future, reduce error of draft observation to millimeters, identify obstacles, explore neural network autonomous learning optimization models, reduce the observation time and labor cost and meet the requirements of diversified application scenarios. At the same time correct the attitude that the accuracy of the current human observation is not high enough or even a negative in the artificial intelligence research area, finally promote the ship, insurance assessment, maritime and other aspects, in order to make the technology which combined artificial observation with intelligent assistance can be applied in practice to solve relevant lawsuits and provide an authoritative legal basis, and be used as the legal basis in the declaration of the ship and subsequent legal disputes.
    VL  - 8
    IS  - 2
    ER  - 

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Author Information
  • Merchant Marine College, Shanghai Maritime University, Shanghai, China

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