Ocean Systems Engineering

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Image-based ship detection using deep learning

  • Lee, Sung-Jun (Department of Naval Architecture and Ocean Engineering, Seoul National University) ;
  • Roh, Myung-Il (Department of Naval Architecture and Ocean Engineering, and Research Institute of Marine Systems Engineering, Seoul National University) ;
  • Oh, Min-Jae (School of Naval Architecture and Ocean Engineering, University of Ulsan)
  • Received : 2020.05.14
  • Accepted : 2020.12.10
  • Published : 2020.12.25
⟨ Previous Next ⟩

Abstract

Detecting objects is important for the safe operation of ships, and enables collision avoidance, risk detection, and autonomous sailing. This study proposes a ship detection method from images and videos taken at sea using one of the state-of-the-art deep neural network-based object detection algorithms. A deep learning model is trained using a public maritime dataset, and results show it can detect all types of floating objects and classify them into ten specific classes that include a ship, speedboat, and buoy. The proposed deep learning model is compared to a universal trained model that detects and classifies objects into general classes, such as a person, dog, car, and boat, and results show that the proposed model outperforms the other in the detection of maritime objects. Different deep neural network structures are then compared to obtain the best detection performance. The proposed model also shows a real-time detection speed of approximately 30 frames per second. Hence, it is expected that the proposed model can be used to detect maritime objects and reduce risks while at sea.

Keywords

Acknowledgement

This work is an expansion of our previous studies (Lee et al. 2018, 2019) and was partially supported by (a) Ministry of Trade, Industry and Energy, Republic of Korea, under "Development of LNG Carrier Performance and Safty Monitoring System Using Satellite Communication and 3D Visualization Technology" (20002720), and (b) Research Institute of Marine Systems Engineering of Seoul National University, Republic of Korea.

References

  1. Cuong, D.D., Hua, X. and Morere, O. (2015), "Maritime vessel images classification using deep convolutional neural networks", Proceedings of the 6th International Symposium on Information and Communication Technology, 276-281.
  2. Everingham, M., Eslami, S.M.A., Van-Gool, L., Williams, C.K.I., Winn, J. and Zisserman, A. (2015), "The PASCAL visual object classes challenge: a retrospective", Int. J. Comput. Vision, 111(1), 98-136. https://doi.org/10.1007/s11263-014-0733-5
  3. Girshick, R., Donahue, J., Darrell, T. and Malik, J. (2014), "Rich feature hierarchies for accurate object detection and semantic segmentation", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580-587.
  4. Hermann, D., Galeazzi, R., Andersen, J.C. and Blanke, M. (2015), "Smart sensor based obstacle detection for high-speed unmanned surface vehicle", IFAC Workshop Series, 48(16), 190-197.
  5. 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", arXiv preprint arXiv, 1611.10012.
  6. ImageNet (2019), URL http://www.image-net.org//
  7. Lee, J.M., Lee, K.H., Nam, B. and Wu, Y. (2016), "Study on image-based ship detection for AR navigation", Proceedings of the 6th International Conference on IT Convergence and Security, ICITCS, 1-4.
  8. Lee, S.J., Roh, M.I., Lee, H.W., Ha, J.S. and Woo, I.G. (2018), "Image-based ship detection and classification for unmanned surface vehicle using real-time object detection neural networks", Proceedings of the International Society of Offshore and Polar Engineers 2018, Sapporo, Japan.
  9. Lee, S.J., Roh, M.I., Oh, M.J., Seok, Y.S., Lee, W.J., Lee, J.B. and Kim, H.S. (2019), "Image-based object detection and tracking method for ship navigation," Proceedings of International Conference on Computer Applications in Shipbuilding 2019, Rotterdam, Netherlands.
  10. Lin, M., Chen, Q. and Yan, S. (2013), "Network in network," arXiv:1312.4400.
  11. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y. and Berg, A.C. (2016), "SSD: single shot multibox detector", Proceedings of European Conference on Computer Vision, Springer, Cham.
  12. Mass, A.L., Hannun, A.Y. and Ng, A.Y. (2013), "Rectifier nonlinearities improve neural network acoustic models", Proceedings of the 30th International Conference on Machine Learning, JMLR: W&CP volume 28.
  13. Pan, S.J. and Yang, Q. (2010), "A survey on transfer learning", IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345-1359. https://doi.org/10.1109/TKDE.2009.191
  14. Prasad, D.K., Rajan, D., Rachmawati, L., Rajabaly, E. and Quek, C. (2017), "Video processing from electrooptical sensors for object detection and tracking in maritime environment: a survey", IEEE T. Intel. Transport. Syst., 18(8), 1993-2016. https://doi.org/10.1109/TITS.2016.2634580
  15. 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.
  16. Redmon, J. and Farhadi, A. (2017), "YOLO9000: better, faster, stronger", Computer Vision and Pattern Recognition, arXiv:1612.08242.
  17. Ren, S., He, K., Girshick, R. and Sun, J. (2015), "Faster R-CNN: towards real-time object detection with region proposal networks", Adv. Neural Inform. Process. Syst., 91-99.
  18. Zhang, R., Yao, J., Zhang, K., Feng, C. and Zhang, J. (2016), "S-CNN-based ship detection from highresolution remote sensing images", International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 41, https://ui.adsabs.harvard.edu/link_gateway/2016ISPAr41B7..423Z/doi:10.5194/isprs-archives-XLI-B7-423-2016.

Cited by

  1. Survey on Deep Learning-Based Marine Object Detection vol.2021, 2020, https://doi.org/10.1155/2021/5808206
  2. Detection and tracking for the awareness of surroundings of a ship based on deep learning vol.8, pp.5, 2020, https://doi.org/10.1093/jcde/qwab053
  3. A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments vol.9, pp.12, 2020, https://doi.org/10.3390/jmse9121408