Skip to main content
SpringerLink
Log in

Ship Detection and Tracking Based on a Custom Aerial Dataset

  • Conference paper
  • First Online:
Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13233))

Included in the following conference series:

  • 1339 Accesses

Abstract

This paper presents an approach based on machine learning techniques for detection and tracking ship in marine environment monitoring, with focus on a custom large data set based on aerial images. The work is placed in the context of autonomous navigation by the use of an unmanned surface naval platform assisted by an aerial drone. The work is according to a data-centric Artificial Intelligence (AI) approach, which involves building AI systems with quality data with a focus on ensuring that the data clearly conveys what the AI must learn. The application of machine learning techniques is used for automatic target detection and tracking. Target information in the surrounding environment allows context-awareness and obstacle identification and it can support naval platform in the management of collision avoidance. The paper focuses on the need of large amounts of data for the training stage to perform robust detections and tracking even in critical glare and waves variations. The paper presents a custom data set which includes fine-tuned public ship aerial images and images acquired by UAV flights over different maritime scenarios. The network’s training results are described and the detection and tracking performance is evaluated in different video sequences from UAV flights over such scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Thombre, S., et al.: Sensors and AI techniques for situational awareness in autonomous ships: a review. IEEE Trans. Intell. Transp. Syst. (2022). https://doi.org/10.1109/TITS.2020.3023957

  2. Cruz, G., Bernardino, A.: Aerial detection in maritime scenarios using convolutional neural networks. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2016. LNCS, vol. 10016, pp. 373–384. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-48680-2_33

    Chapter  Google Scholar 

  3. Cruz, G., Bernardino, A.: Evaluating Aerial Vessel Detector in Multiple Maritime Surveillance Scenarios (2017)

    Google Scholar 

  4. Huixuan, F., Song, G., Wang, Y.: Improved YOLOv4 Marine Target Detection Combined with CBAM (2021). https://doi.org/10.3390/sym13040623

  5. Gu, D., Xu, X., Jin, X.: Marine ship recognition algorithm based on faster-RCNN. Image Signal Process 7, 136–141 (2018)

    Article  Google Scholar 

  6. Qi L., et al.: Ship target detection algorithm based on improved faster R-CNN. Electronics (2019). https://doi.org/10.3390/electronics8090959

  7. Zou Y., Zhao L., Qin S., Pan M., Li Z.: Ship target detection and identification based on SSD MobilenetV2. In: Proceedings of the 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, pp. 1676–1680, (2020). https://doi.org/10.1109/ITOEC49072.2020.9141734

  8. Huang, H., Sun, D., Wang, R., Zhu, C., Liu, B.: Ship target detection based on improved YOLO network. Math. Probl. Eng. (2020). https://doi.org/10.1155/2020/6402149

    Article  Google Scholar 

  9. Chen, X., et al.: Video-based detection infrastructure enhancement for automated ship recognition and behavior analysis (2020). https://doi.org/10.1155/2020/7194342

  10. Huang, Z., Sui, B., Wen, J., Jiang, G.: An intelligent ship image/video detection and classification method with improved regressive deep convolutional neural network. Complexity (2020). https://doi.org/10.1155/2020/1520872

  11. Yildirim, E., Kavzoglu, T.: Ship detection in optical remote sensing images using YOLOv4 and Tiny YOLOv4. In: Ben Ahmed, M., Boudhir, A.A., Kara, I.R., Jain, V., Mellouli, S. (eds.) Innovations in Smart Cities Applications Volume 5. SCA 2021. LNNS, vol. 393, pp. 913–924. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-94191-8_74

  12. Corbane C., Najman L., Pecoul E., Demagistri L., Petit M.: A complete processing chain for ship detection using optical satellite imagery. Int. J. Remote Sens. 5837–5854 (2010). https://doi.org/10.1080/01431161.2010.512310

  13. Yang G., Li B., Ji S., Gao F., Xu Q.: Ship detection from optical satellite images based on sea surface analysis. IEEE Geosci. Remote Sens. Lett. 641–645 (2013). https://doi.org/10.1109/LGRS.2013.2273552

  14. Tang, J., Deng, C., Huang, G., Zhao, B.: Compressed-domain ship detection on space-borne optical image using deep neural network and extreme learning machine. IEEE Trans. Geosci. Remote Sens. 1174–1185 (2014). https://doi.org/10.1109/TGRS.2014.2335751

  15. Qi, S., Ma, J., Lin, J., Li, Y., Tian, J.: Unsupervised ship detection based on saliency and S-HOG descriptor from optical satellite images. IEEE Geosci. Remote Sens. Lett. 1451–1455 (2015). https://doi.org/10.1109/LGRS.2015.2408355

  16. Zou, Z., Shi, Z.: Ship detection in spaceborne optical image with SVD networks. IEEE Trans. Geosci. Remote Sens. 5832–5845 (2016). https://doi.org/10.1109/TGRS.2016.2572736

  17. Liu, Z., Hu, J., Weng, L., Yang, Y.: Rotated region based CNN for ship detection. In: IEEE International Conference on Image Processing (ICIP), pp. 900–904. IEEE (2017). https://doi.org/10.1109/ICIP.2017.8296411

  18. Chang, Y.-L., Anagaw, A., Chang, L., Wang, Y.C., Hsiao, C.-Y., Lee, W-H.: Ship Detection Based on YOLOv2 for SAR Imagery (2019). https://doi.org/10.3390/rs11070786

  19. Chen, Y., Zhang, C., Qiao, T., Xiong, J., Liu, B.: Ship Detection in Optical Sensing Images Based on Yolov5 (2021). https://doi.org/10.1117/12.2589395

  20. Jie, Y., Leonidas, L.A., Mumtaz, F., Ali, M.: Ship Detection and Tracking in Inland Waterways Using Improved YOLOv3 and Deep SORT (2021). https://doi.org/10.3390/sym13020308

  21. Zhang Sr., Y., Shu Sr., J., Hu Sr., L., Zhou Sr., Q., Du Sr., Z.: A ship target tracking algorithm based on deep learning and multiple features. In: Proceedings of the Twelfth International Conference on Machine Vision (ICMV 2019), Amsterdam, The Netherlands (2020). https://doi.org/10.1117/12.2559945

  22. Lee, W.-J., et al.: Detection and tracking for the awareness of surroundings of a ship based on deep learning (2021). https://doi.org/10.1093/jcde/qwab053

  23. Haghbayan, M.-H., et al.: An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environments (2018). https://doi.org/10.1109/ITSC.2018.8569890

  24. Redmon, J., Divvala, S., Girshick, R., Harhali, A.: You Only Look Once: Unified, Real-Time Object Detection (2016). https://arxiv.org/abs/1506.02640

  25. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: YOLOv4: Optimal Speed and Accuracy of Object Detection (2020). https://arxiv.org/pdf/2004.10934.pdf

  26. Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B.: Simple Online and Realtime Tracking (2016). https://arxiv.org/abs/1602.00763

  27. Wojke, N., Bewley, A., Paulus, D.: Simple Online and Realtime Tracking with a DEEP Association Metric (2017). https://arxiv.org/abs/1703.07402

  28. Ribeiro, R., Cruz, G., Matos, J., Bernardino, A.: A Data Set for Airborne Maritime Surveillance Environments (2019). https://doi.org/10.1109/TCSVT.2017.2775524

  29. Gallego, A.-J., Pertusa, A., Gil, P.: Automatic Ship Classification from Optical Aerial Images with Convolutional Neural Networks (2018). https://doi.org/10.3390/rs10040511

  30. Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The PASCAL Visual Object Classes (VOC) challenge. Int. J. Comput. Vis. 88, 303–338 (2010). https://doi.org/10.1007/s11263-009-0275-4

    Article  Google Scholar 

  31. Ira, A., Parico, B., Ahamed, T.: Real time pear fruit detection and counting using YOLOv4 models and deep SORT (2021). https://doi.org/10.3390/s21144803

Download references

Acknowledgements

This study is sponsored by the “MARIN - Monitoraggio Ambientale Remoto Integrato su piattaforma Navale” project (project Code: KATGSO3, “Programma operativo FESR 2014–2020 Obiettivo Convergenza” - Regolamento Regionale n. 17/2014 - Titolo II Capo 1 - “Aiuti ai programmi di investimento delle grandi imprese”), co-funded by Regione Puglia within the framework of “Contratti di Programma”. Project beneficiaries are Fincantieri NexTech S.p.A., RINA Consulting S.p.A. and Co.M.Media s.r.l..

Author information

Authors and Affiliations

  1. Apphia s.r.l., Edificio DHiTech, Via per Monteroni, s.n.c., 73100, Lecce, Italy

    Luigi Paiano, Francesca Calabrese & Marco Cataldo

  2. Fincantieri NexTech S.p.A., Head Office in Milano, Milan, Italy

    Luca Sebastiani & Nicola Leonardi

Authors
  1. Luigi Paiano
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Francesca Calabrese
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Cataldo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luca Sebastiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nicola Leonardi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Luigi Paiano , Francesca Calabrese or Marco Cataldo .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Paiano, L., Calabrese, F., Cataldo, M., Sebastiani, L., Leonardi, N. (2022). Ship Detection and Tracking Based on a Custom Aerial Dataset. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13233. Springer, Cham. https://doi.org/10.1007/978-3-031-06433-3_36

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06433-3_36

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06432-6

  • Online ISBN: 978-3-031-06433-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation