{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T04:57:05Z","timestamp":1775192225072,"version":"3.50.1"},"reference-count":116,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52331012"],"award-info":[{"award-number":["52331012"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52102397"],"award-info":[{"award-number":["52102397"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52071200"],"award-info":[{"award-number":["52071200"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52201403"],"award-info":[{"award-number":["52201403"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52472347"],"award-info":[{"award-number":["52472347"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["23010502000"],"award-info":[{"award-number":["23010502000"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Committee of Science and Technology","award":["52331012"],"award-info":[{"award-number":["52331012"]}]},{"name":"Shanghai Committee of Science and Technology","award":["52102397"],"award-info":[{"award-number":["52102397"]}]},{"name":"Shanghai Committee of Science and Technology","award":["52071200"],"award-info":[{"award-number":["52071200"]}]},{"name":"Shanghai Committee of Science and Technology","award":["52201403"],"award-info":[{"award-number":["52201403"]}]},{"name":"Shanghai Committee of Science and Technology","award":["52472347"],"award-info":[{"award-number":["52472347"]}]},{"name":"Shanghai Committee of Science and Technology","award":["23010502000"],"award-info":[{"award-number":["23010502000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMSE"],"abstract":"<jats:p>The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.<\/jats:p>","DOI":"10.3390\/jmse12101781","type":"journal-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T04:29:26Z","timestamp":1728361766000},"page":"1781","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Visual Navigation Systems for Maritime Smart Ships: A Survey"],"prefix":"10.3390","volume":"12","author":[{"given":"Yuqing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Economics & Management, Shanghai Maritime University, Shanghai 201306, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8959-5108","authenticated-orcid":false,"given":"Xinqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Yuzhen","family":"Wu","sequence":"additional","affiliation":[{"name":"Shandong Port Group, Co., Ltd., Qingdao 266000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2065-9481","authenticated-orcid":false,"given":"Jiansen","family":"Zhao","sequence":"additional","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Octavian","family":"Postolache","sequence":"additional","affiliation":[{"name":"ISCTE\u2014Instituto Universit\u00e1rio de Lisboa, Lisbon University Institute, 1649-004 Lisbon, Portugal"}]},{"given":"Shuhao","family":"Liu","sequence":"additional","affiliation":[{"name":"Merchant Marine College, Shanghai Maritime University, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102764","DOI":"10.1016\/j.apor.2021.102764","article-title":"Risk analysis of grounding accidents by mapping a fault tree into a Bayesian network","volume":"113","author":"Sakar","year":"2021","journal-title":"Appl. Ocean Res."},{"key":"ref_2","first-page":"17","article-title":"Analysis of ship accidents based on European statistical surveys","volume":"68","author":"Zalewski","year":"2021","journal-title":"Zesz. Nauk. Akad. Morskiej Szczecinie"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109675","DOI":"10.1016\/j.ress.2023.109675","article-title":"Accident data-driven human fatigue analysis in maritime transport using machine learning","volume":"241","author":"Fan","year":"2024","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107558","DOI":"10.1016\/j.ress.2021.107558","article-title":"The impact of autonomous ships on safety at sea\u2014A statistical analysis","volume":"210","author":"Hekkenberg","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhang, W., and Zhang, Y. (2023). Navigation Risk Assessment of Autonomous Ships Based on Entropy\u2013TOPSIS\u2013Coupling Coordination Model. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11020422"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"012006","DOI":"10.1088\/1757-899X\/929\/1\/012006","article-title":"Develop and evaluate of intelligent autonomous-ship framework","volume":"929","author":"Moon","year":"2020","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_7","first-page":"17","article-title":"Autonomous shipping and its impact on regulations, technologies, and industries","volume":"4","author":"Kim","year":"2020","journal-title":"J. Int. Marit. Saf. Environ. Aff. Shipp."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"112590","DOI":"10.1016\/j.marpolbul.2021.112590","article-title":"From maritime salvage to IMO 2020 strategy: Two actions to protect the environment","volume":"170","year":"2021","journal-title":"Mar. Pollut. Bull."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"107324","DOI":"10.1016\/j.ress.2020.107324","article-title":"Risk assessment of the operations of maritime autonomous surface ships","volume":"207","author":"Chang","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Fenton, A.J., and Chapsos, I. (2023). Ships without crews: IMO and UK responses to cybersecurity, technology, law and regulation of maritime autonomous surface ships (MASS). Front. Comput. Sci., 5.","DOI":"10.3389\/fcomp.2023.1151188"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1057\/s41278-022-00213-1","article-title":"Operational and economic advantages of autonomous ships and their perceived impacts on port operations","volume":"24","author":"Kurt","year":"2022","journal-title":"Marit. Econ. Logist."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"109380","DOI":"10.1016\/j.oceaneng.2021.109380","article-title":"Collision-avoidance navigation systems for Maritime Autonomous Surface Ships: A state of the art survey","volume":"235","author":"Zhang","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1007\/s13437-022-00277-z","article-title":"Safety challenges related to autonomous ships in mixed navigational environments","volume":"21","author":"Kim","year":"2022","journal-title":"WMU J. Marit. Aff."},{"key":"ref_14","first-page":"39","article-title":"Towards utilizing autonomous ships: A viable advance in industry 4.0","volume":"6","author":"Askari","year":"2022","journal-title":"J. Int. Marit. Saf. Environ. Aff. Shipp."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1080\/16258312.2019.1631714","article-title":"Autonomous ships: A review, innovative applications and future maritime business models","volume":"20","author":"Munim","year":"2019","journal-title":"Supply Chain. Forum Int. J."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, C., Cai, X., Li, Y., Zhai, R., Wu, R., Zhu, S., Guan, L., Luo, Z., Zhang, S., and Zhang, J. (2024). Research and Application of Panoramic Visual Perception-Assisted Navigation Technology for Ships. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12071042"},{"key":"ref_17","first-page":"699","article-title":"Ship recognition and tracking system for intelligent ship based on deep learning framework","volume":"13","author":"Liu","year":"2019","journal-title":"TransNav Int. J. Mar. Navig. Saf. Sea Transp."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6447","DOI":"10.1109\/TGRS.2019.2906054","article-title":"Ship detection based on complex signal kurtosis in single-channel SAR imagery","volume":"57","author":"Leng","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112554","DOI":"10.1016\/j.oceaneng.2022.112554","article-title":"A real-time ship collision risk perception model derived from domain-based approach parameters","volume":"265","author":"Wang","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhou, J., Jiang, P., Zou, A., Chen, X., and Hu, W. (2021). Ship target detection algorithm based on improved YOLOv5. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9080908"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"100204","DOI":"10.1016\/j.cosrev.2019.100204","article-title":"Background subtraction in real applications: Challenges, current models and future directions","volume":"35","author":"Bouwmans","year":"2020","journal-title":"Comput. Sci. Rev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106686","DOI":"10.1016\/j.engappai.2023.106686","article-title":"Orientation-aware ship detection via a rotation feature decoupling supported deep learning approach","volume":"125","author":"Chen","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"230","DOI":"10.1017\/S0373463321000783","article-title":"Lightweight deep network-enabled real-time low-visibility enhancement for promoting vessel detection in maritime video surveillance","volume":"75","author":"Guo","year":"2022","journal-title":"J. Navig."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3811","DOI":"10.1109\/TIV.2023.3347952","article-title":"AiOENet: All-in-one low-visibility enhancement to improve visual perception for intelligent marine vehicles under severe weather conditions","volume":"9","author":"Liu","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, C., Fan, B., Li, Y., Xiao, J., Min, L., Zhang, J., Chen, J., Lin, Z., Su, S., and Wu, R. (2023). Study on the Classification Perception and Visibility Enhancement of Ship Navigation Environments in Foggy Conditions. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11071298"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"109310","DOI":"10.1016\/j.compeleceng.2024.109310","article-title":"Research on inshore ship detection under nighttime low-visibility environment for maritime surveillance","volume":"118","author":"Li","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"046510","DOI":"10.1117\/1.JRS.16.046510","article-title":"Scheme to implement moving target detection of coastal defense radar in complicated sea conditions","volume":"16","author":"Yan","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1109\/JSTARS.2020.3033063","article-title":"Sea-surface floating small target detection by multifeature detector based on isolation forest","volume":"14","author":"Xu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1177\/08438714231202163","article-title":"On-board radio communication and its development in a historical perspective","volume":"36","year":"2024","journal-title":"Int. J. Marit. Hist."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"711","DOI":"10.2112\/SI93-098.1","article-title":"Reliability modeling and analysis of ship communication network based on Apriori algorithm","volume":"93","author":"Xie","year":"2019","journal-title":"J. Coast. Res."},{"key":"ref_31","first-page":"747","article-title":"Shore to Ship Steerable Electromagnetic Beam System Based Ship Communication and Navigation","volume":"28","author":"Hoole","year":"2013","journal-title":"Appl. Comput. Electromagn. Soc. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"56179","DOI":"10.1109\/ACCESS.2024.3391386","article-title":"SQMCR: Stackelberg Q-learning based Multi-hop Cooperative Routing Algorithm for Underwater Wireless Sensor Networks","volume":"12","author":"Wang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"102564","DOI":"10.1016\/j.apor.2021.102564","article-title":"Sliding mode control unified with the uncertainty and disturbance estimator for dynamically positioned vessels subjected to uncertainties and unknown disturbances","volume":"109","author":"Hu","year":"2021","journal-title":"Appl. Ocean Res."},{"key":"ref_34","first-page":"145","article-title":"Adaptive self-regulation PID tracking control for the ship course","volume":"14","author":"Zhao","year":"2019","journal-title":"Chin. J. Ship Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1504\/IJAAC.2022.122596","article-title":"Fuzzy adaptive finite-time sliding mode controller for trajectory tracking of ship course systems with mismatched uncertainties","volume":"16","author":"Hosseinabadi","year":"2022","journal-title":"Int. J. Autom. Control"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wang, S., Er, M.J., Liu, T., and Gong, H. (2023, January 22\u201324). Path Following Control of Underactuated AUV Based on Improved Model Predictive Control. Proceedings of the 2023 6th International Conference on Intelligent Autonomous Systems (ICoIAS), Qinhuangdao, China.","DOI":"10.1109\/ICoIAS61634.2023.00044"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2492","DOI":"10.1007\/s12555-019-0650-z","article-title":"Fuzzy adaptive fixed-time sliding mode control with state observer for a class of high-order mismatched uncertain systems","volume":"18","author":"Abadi","year":"2020","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2019.10.060","article-title":"Q-learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control","volume":"408","author":"Chen","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105778","DOI":"10.1016\/j.ssci.2022.105778","article-title":"A systematic review of human-AI interaction in autonomous ship systems","volume":"152","author":"Veitch","year":"2022","journal-title":"Saf. Sci."},{"key":"ref_40","first-page":"1","article-title":"PSO-based PID controller design for ship course-keeping autopilot","volume":"70","year":"2019","journal-title":"Brodogr. Int. J. Nav. Archit. Ocean Eng. Res. Dev."},{"key":"ref_41","first-page":"174","article-title":"Research of possibilities to increase the exactness of ship stabilizing on a course","volume":"3-3","author":"Volyanskaya","year":"2019","journal-title":"\u041co\u0440\u0441\u043a\u0438\u0435 \u0438\u043d\u0442\u0435\u043b\u043b\u0435\u043a\u0442\u0443\u0430\u043b\u044c\u043d\u044b\u0435 \u0442\u0435\u0445\u043do\u043bo\u0433\u0438\u0438"},{"key":"ref_42","first-page":"23","article-title":"Marine ship\u2019s course stabilization based on an autopilot with a simple fuzzy controller","volume":"25","author":"Volyanskyy","year":"2022","journal-title":"Sci. Bull. Mircea Cel Batran Nav. Acad."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107524","DOI":"10.1016\/j.oceaneng.2020.107524","article-title":"Model predictive control for path following and roll stabilization of marine vessels based on neurodynamic optimization","volume":"217","author":"Liu","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"115883","DOI":"10.1016\/j.oceaneng.2023.115883","article-title":"Ship roll stabilization using an adaptive fractional-order sliding mode controller","volume":"287","author":"Rezaei","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"114306","DOI":"10.1016\/j.oceaneng.2023.114306","article-title":"An anti-rolling control method of rudder fin system based on ADRC decoupling and DDPG parameter adjustment","volume":"278","author":"Sun","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"872","DOI":"10.1007\/s00773-020-00779-6","article-title":"Course-keeping with roll damping control for ships using rudder and fin","volume":"26","author":"Zhang","year":"2021","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"181880","DOI":"10.1109\/ACCESS.2020.2992458","article-title":"Model predictive control of a shipborne hydraulic parallel stabilized platform based on ship motion prediction","volume":"8","author":"Qiang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_48","unstructured":"You, X., Li, S., Liu, J., and Yan, X. (2023, January 19\u201323). Experimental research of the PID tune method for ship path following control. Proceedings of the ISOPE International Ocean and Polar Engineering Conference, Ottawa, ON, Canada."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"106726","DOI":"10.1016\/j.oceaneng.2019.106726","article-title":"FAILOS guidance law based adaptive fuzzy finite-time path following control for underactuated MSV","volume":"195","author":"Nie","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"108863","DOI":"10.1016\/j.automatica.2020.108863","article-title":"Distributed implementation of nonlinear model predictive control for AUV trajectory tracking","volume":"115","author":"Shen","year":"2020","journal-title":"Automatica"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.neucom.2019.08.090","article-title":"MLP neural network-based recursive sliding mode dynamic surface control for trajectory tracking of fully actuated surface vessel subject to unknown dynamics and input saturation","volume":"377","author":"Shen","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"110516","DOI":"10.1016\/j.oceaneng.2021.110516","article-title":"Active disturbance rejection control for ship path following with Euler method","volume":"247","author":"Zhang","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_53","unstructured":"Liu, D., Yao, C., Yu, J., Feng, D., and Sun, X. (2024, January 16\u201321). Trajectory Tracking Control of an Intelligent Ship Based on Deep Reinforcement Learning. Proceedings of the ISOPE International Ocean and Polar Engineering Conference, Rhodes, Greece."},{"key":"ref_54","first-page":"1","article-title":"Tracking control of ships based on ADRC-MFAC","volume":"18","author":"Li","year":"2023","journal-title":"Chin. J. Ship Res"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"24302","DOI":"10.1109\/ACCESS.2022.3154812","article-title":"AIS-based intelligent vessel trajectory prediction using bi-LSTM","volume":"10","author":"Yang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"108734","DOI":"10.1016\/j.oceaneng.2021.108734","article-title":"Concise robust fuzzy nonlinear feedback track keeping control for ships using multi-technique improved LOS guidance","volume":"224","author":"Min","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, H., Chen, H., Gao, N., A\u03cat-Ahmed, N., Charpentier, J.-F., and Benbouzid, M. (2022). Ship dynamic positioning control based on active disturbance rejection control. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10070865"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Papadimitrakis, M., Stogiannos, M., Sarimveis, H., and Alexandridis, A. (2021). Multi-ship control and collision avoidance using MPC and RBF-based trajectory predictions. Sensors, 21.","DOI":"10.3390\/s21216959"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.ssci.2019.09.018","article-title":"Ship collision avoidance methods: State-of-the-art","volume":"121","author":"Huang","year":"2020","journal-title":"Saf. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Ding, Z. (2021, January 18\u201319). A ship-motion prediction algorithm based on modified covariance method and neural networks. Proceedings of the International Conference on Computer Application and Information Security (ICCAIS 2021), Wuhan, China.","DOI":"10.1117\/12.2637398"},{"key":"ref_61","first-page":"103","article-title":"Collision-avoidance path planning for multi-ship encounters considering ship manoeuvrability and COLREGs","volume":"3","author":"He","year":"2021","journal-title":"Transp. Saf. Environ."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"100438","DOI":"10.1016\/j.ijnaoe.2022.100438","article-title":"Collision avoidance based on predictive probability using Kalman filter","volume":"14","author":"Kim","year":"2022","journal-title":"Int. J. Nav. Archit. Ocean Eng."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"111527","DOI":"10.1016\/j.oceaneng.2022.111527","article-title":"Ship trajectory planning for collision avoidance using hybrid ARIMA-LSTM models","volume":"256","author":"Abebe","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"110143","DOI":"10.1016\/j.oceaneng.2021.110143","article-title":"Conflict detection method based on dynamic ship domain model for visualization of collision risk Hot-Spots","volume":"242","author":"Liu","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"107766","DOI":"10.1016\/j.ress.2021.107766","article-title":"A ship domain-based model of collision risk for near-miss detection and Collision Alert Systems","volume":"214","author":"Szlapczynski","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"112895","DOI":"10.1016\/j.oceaneng.2022.112895","article-title":"Ship collision risk analysis: Modeling, visualization and prediction","volume":"266","author":"Liu","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"161969","DOI":"10.1109\/ACCESS.2020.3013957","article-title":"Ship collision risk assessment based on collision detection algorithm","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"107772","DOI":"10.1016\/j.ress.2021.107772","article-title":"A probabilistic risk approach for the collision detection of multi-ships under spatiotemporal movement uncertainty","volume":"215","author":"Xin","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Shi, J., and Liu, Z. (2022). Track pairs collision detection with applications to ship collision risk assessment. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10020216"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1007\/s00773-010-0106-x","article-title":"Fuzzy logic based decision making system for collision avoidance of ocean navigation under critical collision conditions","volume":"16","author":"Perera","year":"2011","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Park, J., and Jeong, J.-S. (2021). An estimation of ship collision risk based on relevance vector machine. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9050538"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"106718","DOI":"10.1016\/j.oceaneng.2019.106718","article-title":"An improved time discretized non-linear velocity obstacle method for multi-ship encounter detection","volume":"196","author":"Chen","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"11148","DOI":"10.1109\/TITS.2021.3101007","article-title":"Ship collision avoidance utilizing the cross-entropy method for collision risk assessment","volume":"23","author":"Tengesdal","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"117585","DOI":"10.1016\/j.oceaneng.2024.117585","article-title":"Novel collision risk measurement method for multi-ship encounters via velocity obstacles and temporal proximity","volume":"302","author":"Cheng","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"106511","DOI":"10.1016\/j.oceaneng.2019.106511","article-title":"Optimizing the joint collision avoidance operations of multiple ships from an overall perspective","volume":"191","author":"Li","year":"2019","journal-title":"Ocean Eng."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"113737","DOI":"10.1016\/j.oceaneng.2023.113737","article-title":"Automatic collision avoidance algorithm based on route-plan-guided artificial potential field method","volume":"271","author":"Zhu","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ren, J., Zhang, J., and Cui, Y. (2021). Autonomous obstacle avoidance algorithm for unmanned surface vehicles based on an improved velocity obstacle method. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10090618"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Zhang, L., Mou, J., Chen, P., and Li, M. (2021). Path planning for autonomous ships: A hybrid approach based on improved apf and modified vo methods. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9070761"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"110749","DOI":"10.1016\/j.oceaneng.2022.110749","article-title":"COLREGs-abiding hybrid collision avoidance algorithm based on deep reinforcement learning for USVs","volume":"247","author":"Xu","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Yang, Z., Jing, Q., and Li, X. (2023). Dynamic Data-Driven Ship Motion Simulation toward Visual-Aided Navigation on Water. Water, 15.","DOI":"10.3390\/w15050872"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Chen, X., Dou, S., Song, T., Wu, H., Sun, Y., and Xian, J. (2024). Spatial-Temporal Ship Pollution Distribution Exploitation and Harbor Environmental Impact Analysis via Large-Scale AIS Data. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12060960"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"21596","DOI":"10.1109\/ACCESS.2024.3349957","article-title":"A transformer network with sparse augmented data representation and cross entropy loss for ais-based vessel trajectory prediction","volume":"12","author":"Nguyen","year":"2024","journal-title":"IEEE Access"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Zhu, J., Gao, M., Zhang, A., Hu, Y., and Zeng, X. (2022). Multi-ship encounter situation identification and analysis based on AIS data and graph complex network theory. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10101536"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"110479","DOI":"10.1016\/j.oceaneng.2021.110479","article-title":"Ship collision avoidance behaviour recognition and analysis based on AIS data","volume":"245","author":"Rong","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"114755","DOI":"10.1016\/j.eswa.2021.114755","article-title":"Visual analytic based ship collision probability modeling for ship navigation safety","volume":"175","author":"Boz","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"He, W., Lei, J., Chu, X., Xie, S., Zhong, C., and Li, Z. (2021). A visual analysis approach to understand and explore quality problems of AIS data. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9020198"},{"key":"ref_87","first-page":"21","article-title":"Enhancing Maritime Navigation Safety through AIS-Based Visual Augmentation: A Deep Learning Approach to Integrating Real and Virtual Views","volume":"3","author":"Carter","year":"2023","journal-title":"J. Comput. Sci. Softw. Appl."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Xia, R., Chen, J., Huang, Z., Wan, H., Wu, B., Sun, L., Yao, B., Xiang, H., and Xing, M. (2022). CRTransSar: A visual transformer based on contextual joint representation learning for SAR ship detection. Remote Sens., 14.","DOI":"10.3390\/rs14061488"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Chen, Z., Ding, Z., Zhang, X., Wang, X., and Zhou, Y. (2023). Inshore ship detection based on multi-modality saliency for synthetic aperture radar images. Remote Sens., 15.","DOI":"10.3390\/rs15153868"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Mou, X., Chen, X., Guan, J., Chen, B., and Dong, Y. (2019, January 23\u201326). Marine target detection based on improved faster R-CNN for navigation radar PPI images. Proceedings of the 2019 International Conference on Control, Automation and Information Sciences (ICCAIS), Chengdu, China.","DOI":"10.1109\/ICCAIS46528.2019.9074588"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"280","DOI":"10.51400\/2709-6998.1433","article-title":"Sea ice warning visualization and path planning for ice navigation based on radar image recognition","volume":"29","author":"Hsieh","year":"2021","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"108630","DOI":"10.1016\/j.measurement.2020.108630","article-title":"Assessment of ship position estimation accuracy based on radar navigation mark echoes identified in an Electronic Navigational Chart","volume":"169","author":"Naus","year":"2021","journal-title":"Measurement"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1631\/FITEE.2000611","article-title":"Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images","volume":"23","author":"Chen","year":"2022","journal-title":"Front. Inf. Technol. Electron. Eng."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Li, Z., Pan, M., Hu, J., and Guo, J. (2022, January 4\u20136). Design on ship \u201cvideo radar\u201d enhanced navigation system based on multi-camera. Proceedings of the 2022 5th International Conference on Signal Processing and Machine Learning, Dalian, China.","DOI":"10.1145\/3556384.3556392"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"11107","DOI":"10.1109\/TITS.2023.3281547","article-title":"A novel ship speed and heading estimation approach using radar sequential images","volume":"24","author":"Xu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_96","first-page":"1","article-title":"Background modeling combined with multiple features in the Fourier domain for maritime infrared target detection","volume":"60","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.infrared.2019.04.018","article-title":"Infrared target detection in backlighting maritime environment based on visual attention model","volume":"99","author":"Dong","year":"2019","journal-title":"Infrared Phys. Technol."},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Liu, Z., He, J., Zhang, T., Tang, R., Li, Y., and Waqas, M. (2022, January 15\u201320). Infrared ship video target tracking based on cross-connection and spatial transformer network. Proceedings of the International Conference on Artificial Intelligence and Security, Qinghai, China.","DOI":"10.1007\/978-3-031-06788-4_9"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Cao, Y., Cheng, W., Wang, X., and Huang, Y. (2023, January 26\u201328). Research on Ship Target Recognition based on Infrared Image Method. Proceedings of the 2023 4th International Conference on Computing, Networks and Internet of Things, Xiamen, China.","DOI":"10.1145\/3603781.3603814"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Gao, Z., Zhang, Y., and Wang, S. (2023). Lightweight Small Ship Detection Algorithm Combined with Infrared Characteristic Analysis for Autonomous Navigation. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11061114"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"100608","DOI":"10.1016\/j.ijnaoe.2024.100608","article-title":"Multi-vessel Target Tracking with Camera Fusion for Unmanned Surface Vehicles","volume":"16","author":"Park","year":"2024","journal-title":"Int. J. Nav. Archit. Ocean Eng."},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Li, Y., Tao, K., Li, X., and Wang, F. (2019, January 14\u201317). Research on Visual Laser Navigation of Ships. Proceedings of the 2019 5th International Conference on Transportation Information and Safety (ICTIS), Liverpool, UK.","DOI":"10.1109\/ICTIS.2019.8883787"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"32767","DOI":"10.1109\/ACCESS.2020.2973856","article-title":"Visual recognition based on deep learning for navigation mark classification","volume":"8","author":"Pan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_104","doi-asserted-by":"crossref","unstructured":"Shao, Z., Lyu, H., Yin, Y., Cheng, T., Gao, X., Zhang, W., Jing, Q., Zhao, Y., and Zhang, L. (2022). Multi-scale object detection model for autonomous ship navigation in maritime environment. J. Mar. Sci. Eng., 10.","DOI":"10.3390\/jmse10111783"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"11743","DOI":"10.1109\/TITS.2023.3287709","article-title":"Ship Collision Avoidance Navigation Signal Recognition via Vision Sensing and Machine Forecasting","volume":"24","author":"Bi","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"114198","DOI":"10.1016\/j.oceaneng.2023.114198","article-title":"Improving maritime traffic surveillance in inland waterways using the robust fusion of AIS and visual data","volume":"275","author":"Qu","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"G\u00fclsoylu, E., Koch, P., Yildiz, M., Constapel, M., and Kelm, A.P. (2024, January 3\u20138). Image and AIS Data Fusion Technique for Maritime Computer Vision Applications. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Waikoloa, HI, USA.","DOI":"10.1109\/WACVW60836.2024.00098"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"118242","DOI":"10.1016\/j.oceaneng.2024.118242","article-title":"A robust assessment of inland waterway collision risk based on AIS and visual data fusion","volume":"307","author":"Ding","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1017\/S0373463319000481","article-title":"Fusion of ship perceptual information for electronic navigational chart and radar images based on deep learning","volume":"73","author":"Guo","year":"2020","journal-title":"J. Navig."},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Zhang, C., Fang, M., Yang, C., Yu, R., and Li, T. (2021). Perceptual fusion of electronic chart and marine radar image. J. Mar. Sci. Eng., 9.","DOI":"10.3390\/jmse9111245"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Gao, Z., Zhu, F., Chen, H., and Ma, B. (2022). Maritime Infrared and Visible Image Fusion Based on Refined Features Fusion and Sobel Loss. Photonics, 9.","DOI":"10.3390\/photonics9080566"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Jeon, R., and Jones, N. (2023, January 25\u201328). Visual and Infrared Detection and Ranging (VAIDAR) for Marine Navigational Hazards. Proceedings of the OCEANS 2023-MTS\/IEEE US Gulf Coast, Biloxi, MI, USA.","DOI":"10.23919\/OCEANS52994.2023.10336995"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"116133","DOI":"10.1016\/j.oceaneng.2023.116133","article-title":"AIS aided marine radar target tracking in a detection occluded environment","volume":"288","author":"Sun","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"5688","DOI":"10.1109\/TITS.2023.3338293","article-title":"Integration of Radar Sequential Images and AIS for Ship Speed and Heading Estimation Under Uncertainty","volume":"25","author":"Xu","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.measurement.2022.111630","article-title":"A new multi-sensor fusion approach for integrated ship motion perception in inland waterways","volume":"200","author":"Wu","year":"2022","journal-title":"Measurement"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Xiao, G., and Xu, L. (2024). Challenges and Opportunities of Maritime Transport in the Post-Epidemic Era. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12091685"}],"container-title":["Journal of Marine Science and Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-1312\/12\/10\/1781\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:08:57Z","timestamp":1760112537000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-1312\/12\/10\/1781"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":116,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["jmse12101781"],"URL":"https:\/\/doi.org\/10.3390\/jmse12101781","relation":{},"ISSN":["2077-1312"],"issn-type":[{"value":"2077-1312","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,8]]}}}