Hostname: page-component-745bb68f8f-b95js Total loading time: 0 Render date: 2025-01-15T22:56:48.776Z Has data issue: false hasContentIssue false
  • English
  • Français

Enhancing the function of the aids to navigation by practical usage of the deep learning algorithm

Published online by Cambridge University Press:  13 January 2025

Yoontae Sim Open the ORCID record for Yoontae Sim [Opens in a new window]  and
Chong-Ju Chae
Yoontae Sim*
Affiliation:
Division of Navigation safety system, Mopko Maritime University, Mokpo, Korea
Chong-Ju Chae
Affiliation:
Maritime Safety & Environmental Administration (MSEA), World Maritime University, Malmo, Sweden
*
*Corresponding author: Yoontae Sim; Email: 12188@naver.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Information is provided to navigators through advanced onboard navigation equipment, such as the electronic chart display and information system (ECDIS), radar and the automatic identification system (AIS). However, maritime accidents still occur, especially in coastal and inland water where many navigational dangers exist. The recent artificial intelligence (AI) technology is actively applied in navigation fields, such as collision avoidance and ship detection. However, utilising the aids to navigation (AtoN) system requires more engagement and further exploration. The AtoN system provides critical navigation information by marking the navigation hazards, such as shallow water areas and wrecks, and visually marking narrow passageways. The prime function of the AtoN can be enhanced by applying AI technology, particularly deep learning technology. With the help of this technology, an algorithm could be constructed to detect AtoN in coastal and inland waters and utilise the detected AtoN to create a safety function to supplement watchkeepers using recent navigation equipment.

Keywords

artificial intelligencedeep learning algorithmobject detectionAtoN detection
Type
Research Article
Information
The Journal of Navigation , First View , pp. 1 - 12
Check for updates
Copyright
Copyright © The Author(s), 2025. Published by Cambridge University Press on behalf of The Royal Institute of Navigation

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

a5. Masan port navigation. Narrow channel. Masan passage. Fairway. Ship. Video clip from Youtube. https://www.youtube.com/watch?v=Vyc5X7noePQGoogle Scholar
AI-Hub. ‘Nautical Object Image data’ Retrieved from: https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=175Google Scholar
Back in Port Qasim, Pakistan on Maersk Container Vessel after almost 2 years. Video clip from Youtube. https://www.youtube.com/watch?v=VLpy9L1NyJY&t=3sGoogle Scholar
Bochkovskiy, A., Wang, C. Y. and Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv (Cornell University). [Preprint]. Available at https://arxiv.org/pdf/2004.10934v1Google Scholar
Bolbot, V., Gkerekos, C., Theotokatos, G. and Boulougouris, E. (2022). Automatic traffic scenarios generation for autonomous ships collision avoidance system testing. Ocean Engineering, 254, 111309. doi:10.1016/j.oceaneng.2022.111309CrossRefGoogle Scholar
Carneiro, T., Da Nóbrega, R. V. M., Nepomuceno, T., Bian, G. B., De Albuquerque, V. H. C. and Reboucas Filho, P. P. (2018). Performance analysis of google colaboratory as a tool for accelerating deep learning applications. IEEE Access, 6, 61677–61685. doi:10.1109/access.2018.2874767CrossRefGoogle Scholar
Choe, C., Noh, Y., Shin, D., Kim, H. and Park, H. (2021). Identifying risk factors of marine accidents in coastal area by marine accident types. Daehan Gyotong Haghoeji, 39(4), 540–554. doi:10.7470/jkst.2021.39.4.540Google Scholar
Dominguez-Péry, C., Vuddaraju, L. N. R., Corbett-Etchevers, I. and Tassabehji, R. (2021). Reducing maritime accidents in ships by tackling human error: A bibliometric review and research agenda. Journal of Shipping and Trade, 6(1), 1–32. doi:10.1186/s41072-021-00098-yCrossRefGoogle Scholar
Du, J. (2018). Understanding of object detection based on CNN family and YOLO. Journal of Physics: Conference Series, 1004, 012029. doi:10.1088/1742-6596/1004/1/012029Google Scholar
Du, Y., Sun, S., Qiu, S., Li, S., Pan, M. and Chen, C. (2021). Intelligent recognition system based on contour accentuation for navigation marks. Wireless Communications and Mobile Computing, 2021, 1–11. doi:10.1155/2021/6631074CrossRefGoogle Scholar
Escorcia-Gutierrez, J., Beleño, K., Jimenez-Cabas, J., Elhoseny, M., Dahman Alshehri, M. and Selim, M. M. (2022). Intelligent deep learning-enabled autonomous small ship detection and classification model. Computers & Electrical Engineering, 100, 107871. doi:10.1016/j.compeleceng.2022.107871CrossRefGoogle Scholar
Giant Merchant vessel passing Columbia River channel in USA. Video clip from Youtube. https://www.youtube.com/watch?v=I4cKzeeKG5EGoogle Scholar
Guo, M., Guo, C., Zhang, C., Zhang, D. and Gao, Z. (2020). Fusion of ship perceptual information for electronic navigational chart and radar images based on deep learning. The Journal of Navigation, 73(1), 192–211.CrossRefGoogle Scholar
Hammedi, W., Ramirez-Martinez, M., Brunet, P., Senouci, S. M. and Messous, M. A. (2019). Deep Learning-Based Real-Time Object Detection in Inland Navigation. In 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, pp. 1–6.CrossRefGoogle Scholar
Han, X., Pan, M., Ge, H., Li, S., Hu, J., Zhao, J. and Li, Y. (2021). Multilabel video classification model of navigation mark's lights based on deep learning. Computational Intelligence and Neuroscience, 2021, 6794202, 13 pages, doi:10.1155/2021/6794202CrossRefGoogle ScholarPubMed
Huang, J., Rathod, V., Sun, C., Zhu, M., Korattikara, A., Fathi, A., Fischer, I., Wojna, Z., Song, Y., Guadarrama, S. and Murphy, K. (2016). Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors.CrossRefGoogle Scholar
Jocherr, G. (2020). YOLOv5. (GitHub) Retrieved from https://docs.ultralytics.com/Google Scholar
Jurdziński, M. (2017). Causes of ships groundings in terms of integrated navigation model. Annual of Navigation, 24, 119–135.CrossRefGoogle Scholar
Karimi, G. (2021). ‘Introduction to YOLO Algorithm for Object Detection’. Retrieved from https://www.section.io/engineering-education/introduction-to-yolo-algorithm-for-object-detection/Google Scholar
Lee, J. M., Lee, K. H., Nam, B. and Wu, Y. (2016). Study on Image-Based Ship Detection for AR Navigation. 2016 6th International Conference on IT Convergence and Security (ICITCS). IEEE, pp. 1. doi:10.1109/ICITCS.2016.7740373.CrossRefGoogle Scholar
Lee, S., Roh, M., Lee, H., Ha, J. and Woo, I. (2018). Image-based Ship Detection and Classification for Unmanned Surface Vehicle Using Real-Time Object Detection Neural Networks. The 28th International Ocean and Polar Engineering Conference.Google Scholar
Mason, E., Yonel, B. and Yazici, B. (2017). Deep Learning for Radar. 2017 IEEE Radar Conference (RadarConf), pp. 1703–1708.CrossRefGoogle Scholar
Mohanty, S. P., Czakon, J., Kaczmarek, K. A., Pyskir, A., Tarasiewicz, P., Kunwar, S., Rohrbach, J., Luo, D., Prasad, M. and Fleer, S. (2020). Deep learning for understanding satellite imagery: An experimental survey. Frontiers in Artificial Intelligence, 3, 534696.CrossRefGoogle ScholarPubMed
Murray, B. and Perera, L. P. (2021). An AIS-based deep learning framework for regional ship behavior prediction. Reliability Engineering & System Safety, 215, 107819.CrossRefGoogle Scholar
Nelson, M. J. and Hoover, A. K. (2020). Notes on Using Google Colaboratory in AI Education. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, pp. 533–534.CrossRefGoogle Scholar
O'Shea, K. and Nash, R. (2015). An introduction to convolutional neural networks. arXiv preprint arXiv:1511.08458.Google Scholar
Redmon, J. and Farhadi, A. (2016). YOLO9000: Better, faster, stronger. arXiv preprint arXiv, 1612.08242.Google Scholar
Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.Google Scholar
Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788.CrossRefGoogle Scholar
Ren, X., Li, X., Ren, K., Song, J., Xu, Z., Deng, K. and Wang, X. (2021). Deep learning-based weather prediction: A survey. Big Data Research, 23, 100178.CrossRefGoogle Scholar
Rodrigues, E. R., Oliveira, I., Cunha, R. and Netto, M. (2018). DeepDownscale: A Deep Learning Strategy for High-Resolution Weather Forecast. 2018 IEEE 14th International Conference on e-Science (e-Science), pp. 415–422.CrossRefGoogle Scholar
Rothblum, A. M. (2000). Human Error and Marine Safety. National Safety Council Congress and Expo, Orlando, FL.Google Scholar
Sahin, B. and Yip, T. (2020). Analysis of Root Causes for Maritime Accidents Originated From Human Factor. IAME 2020 Conference, 10-13 June, PolyU, Hong Kong.Google Scholar
Salman, A. G., Kanigoro, B. and Heryadi, Y. (2015). Weather Forecasting Using Deep Learning Techniques. 2015 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 281–285.CrossRefGoogle Scholar
Shi, Q., Li, W., Zhang, F., Hu, W., Sun, X. and Gao, L. (2018). Deep CNN with multi-scale rotation invariance features for ship classification. IEEE Access, 6, 38656–38668.CrossRefGoogle Scholar
Thuan, D. (2021). Evolution of Yolo Algorithm and Yolov5: The State-of-the-Art Object Detention Algorithm. Retrieved from https://www.theseus.fi/handle/10024/452552Google Scholar
Time Lapse Port of Rotterdam Dolwin Alpha. Video clip from Youtube. https://www.youtube.com/watch?v=SadEpzNfxtk&t=168sGoogle Scholar
Wang, X., Liu, J. and Liu, X. (2022). Ship feature recognition methods for deep learning in complex marine environments. Complex & Intelligent. Systems, 8, 3881–3897.CrossRefGoogle Scholar
Zhang, R., Yao, J., Zhang, K., Feng, C. and Zhang, J. (2016). S-CNN-based Ship Detection From High-Resolution Remote Sensing Images. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, Vol. 41.Google Scholar