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2024 | OriginalPaper | Buchkapitel

Marine Vision-Based Situational Automatic Ship Detection Using Remote Sensing Images

verfasst von : Anandakumar Haldorai, Babitha Lincy R, Suriya Murugan, Minu Balakrishnan

Erschienen in: Artificial Intelligence for Sustainable Development

Verlag: Springer Nature Switzerland

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Abstract

Object detection or identification is the one of the fundamental problems in computer vision application. Even though, object detection is a successful research area, detection of small object from remote sensing images is complicated. Remote sensing image-based automatic ship detection is part of marine surveillance system. Marine safety is also one of the main security sectors for national security. So, to avoid the risk of pirates and extremists entering the harbor zones, early detection of ship is necessary. Similarly, when there are accidents of ships in maritime, identifying the ship is a challengeable task. So, when considering oceanic security and safety, automatic detection of ship is obligatory. The deep learning model, particularly MobileNet, without forgetting architecture, was considered for automatic and early ship detection. The experimental setup produced the 98.2% accuracy rate with Kaggle ship dataset. The experimental setup is evaluated with the performance analysis and finally compared with some other techniques.

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T. Zhang, Z. Zhang, H. Yang, W. Guo and Z. Yang, “Ship Detection of Polarimetric SAR Images Using a Nonlocal Spatial Information-Guided Method,” in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, Art no. 4513805, https://doi.org/10.1109/LGRS.2022.3205619.

M.-D. Li, X.-C. Cui and S.-W. Chen, “Adaptive Superpixel-Level CFAR Detector for SAR Inshore Dense Ship Detection,” in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, Art no. 4010405, https://doi.org/10.1109/LGRS.2021.3059253.

M. Zha, W. Qian, W. Yang and Y. Xu, “Multifeature Transformation and Fusion-Based Ship Detection With Small Targets and Complex Backgrounds,” in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, Art no. 4511405, https://doi.org/10.1109/LGRS.2022.3192559.

Y. Zhuang, Y. Liu, T. Zhang and H. Chen, “Contour Modeling Arbitrary-Oriented Ship Detection From Very High-Resolution Optical Remote Sensing Imagery,” in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1–5, 2023, Art no. 6000805, https://doi.org/10.1109/LGRS.2023.3239016.

Bukuroshe Elira Epoka, “Literature Review of Qualitative Data with Natural Language Processing”, Journal of Robotics Spectrum, vol.1, pp. 056–065, 2023. https://doi.org/10.53759/9852/JRS202301006.

K. Naveen Durai, R. Subha, and A. Haldorai, “Hybrid Invasive Weed Improved Grasshopper Optimization Algorithm for Cloud Load Balancing,” Intelligent Automation Soft Computing, vol. 34, no. 1, pp. 467–483, 2022, https://doi.org/10.32604/iasc.2022.026020.

Xia Hao, “Examining Collaborative Business Process Modeling Techniques”, Journal of Enterprise and Business Intelligence, vol.3, no.2, pp. 075–084, April 2023. https://doi.org/10.53759/5181/JEBI202303008.

Md. Z. Hussain, M. Ashraf, D. K. Singh, A. Haldorai, D. K. Mishra, and T. N. Shanavas, “Intelligent data post and read data system like to feed for IoT sensors,” International Journal of System Assurance Engineering and Management, Jun. 2022, https://doi.org/10.1007/s13198-022-01683-5.

Y. Li, S. Zhang and W.-Q. Wang, “A Lightweight Faster R-CNN for Ship Detection in SAR Images,” in IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022, Art no. 4006105, https://doi.org/10.1109/LGRS.2020.3038901.

10.

Q. Bai, G. Gao, X. Zhang, L. Yao and C. Zhang, “LSDNet: Lightweight CNN Model Driven by PNF for PolSAR Image Ship Detection,” in IEEE Journal on Miniaturization for Air and Space Systems, vol. 3, no. 3, pp. 135–142, Sept. 2022, https://doi.org/10.1109/JMASS.2022.3203082.

11.

B. Wang, B. Han and L. Yang, “Accurate Real-time Ship Target detection Using Yolov4,” 2021 6th International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 2021, pp. 222–227, https://doi.org/10.1109/ICTIS54573.2021.9798495.

12.

S. Lei, Y. Zhao and Y. Bai, “Ship detection based on deep learning under complex lighting,” 2022 34th Chinese Control and Decision Conference (CCDC), Hefei, China, 2022, pp. 5694–5698, https://doi.org/10.1109/CCDC55256.2022.10033941.

13.

J. Li, J. Tian, P. Gao and L. Li, “Ship Detection and Fine-Grained Recognition in Large-Format Remote Sensing Images Based on Convolutional Neural Network,” IGARSS 2020—2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 2859–2862, https://doi.org/10.1109/IGARSS39084.2020.9323246.

14.

Y. Chen, B. Zhang, Z. Li and Y. Qiao, “Ship Detection with Optical Image Based on Attention and Loss Improved YOLO,” 2022 3rd International Conference on Pattern Recognition and Machine Learning (PRML), Chengdu, China, 2022, pp. 1–5, https://doi.org/10.1109/PRML56267.2022.9882217.

15.

M. Jianying, Z. Di and Y. Risheng, “Ship detection using spaceborne rotating fan-beam radar,” 2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China, 2021, pp. 1301–1304, https://doi.org/10.1109/Radar53847.2021.10028518.

16.

Y. Zhang, M. Xing and J. Zhang, “SAR Ship Targets Detection Based on Correlation Filters,” 2021 CIE International Conference on Radar (Radar), Haikou, Hainan, China, 2021, pp. 1297–1300, https://doi.org/10.1109/Radar53847.2021.10028184.

17.

S. Wei, H. Chen, X. Zhu and H. Zhang, “Ship Detection in Remote Sensing Image based on Faster R-CNN with Dilated Convolution,” 2020 39th Chinese Control Conference (CCC), Shenyang, China, 2020, pp. 7148–7153, https://doi.org/10.23919/CCC50068.2020.9189467.

18.

S. Zhou and J. Yin, “YOLO-Ship: A Visible Light Ship Detection Method,” 2022 2nd International Conference on Consumer Electronics and Computer Engineering (ICCECE), Guangzhou, China, 2022, pp. 113–118, https://doi.org/10.1109/ICCECE54139.2022.9712768.

19.

X. Leng, J. Wang, K. Ji and G. Kuang, “Ship Detection in Range-Compressed SAR Data,” IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 2135–2138, https://doi.org/10.1109/IGARSS46834.2022.9884909.

20.

J. Yuan, Z. Zhang and P. Zhang, “Ship Detection of SAR Image in Complex Nearshore Environment,” 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV), Singapore, Singapore, 2022, pp. 317–321, https://doi.org/10.1109/ICARCV57592.2022.10004222.

21.

Y. Bai, S. Lei and L. Liu, “The Ship Target Detection Based on Sea-Sky-Line,” 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China, 2021, pp. 456–460, https://doi.org/10.1109/CACRE52464.2021.9501336.

22.

Y. Zou, L. Zhao, S. Qin, M. Pan and Z. Li, “Ship target detection and identification based on SSD_MobilenetV2,” 2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, 2020, pp. 1676–1680, https://doi.org/10.1109/ITOEC49072.2020.9141734.

23.

L. Ying, D. Miao, Z. Zhang and Q. Zhang, “Remote Sensing Ship Detection Based on Three-way Decisions and Multi-granularity Feature Fusion,” 2022 IEEE 8th International Conference on Cloud Computing and Intelligent Systems (CCIS), Chengdu, China, 2022, pp. 234–238, https://doi.org/10.1109/CCIS57298.2022.10016364.

24.

Y. Zhang, M. J. Er, W. Gao and J. Wu, “High Performance Ship Detection via Transformer and Feature Distillation,” 2022 5th International Conference on Intelligent Autonomous Systems (ICoIAS), Dalian, China, 2022, pp. 31–36, https://doi.org/10.1109/ICoIAS56028.2022.9931223.

25.

C. Zhang, P. Liu, H. Wang and Y. Jin, “Saliency-Based Centernet for Ship Detection in SAR Images,” IGARSS 2022—2022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022, pp. 1552–1555, https://doi.org/10.1109/IGARSS46834.2022.9883396.

26.

C. Yu and Y. Shin, “A Deep Learning-Based SAR Ship Detection,” 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Bali, Indonesia, 2023, pp. 744–747, https://doi.org/10.1109/ICAIIC57133.2023.10067131.

27.

Y. Lei, X. Leng and K. Ji, “Marine Ship Target Detection in SAR Image Based on Google Earth Engine,” 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 8574–8577, https://doi.org/10.1109/IGARSS47720.2021.9553711.

28.

N. Wang and F. Ma, “Single ship target detection based on the concept of edge computing,” 2021 6th International Conference on Transportation Information and Safety (ICTIS), Wuhan, China, 2021, pp. 559–564, https://doi.org/10.1109/ICTIS54573.2021.9798642.

29.

K. Zhao, Y. Zhou and X. Chen, “A Dense Connection Based SAR Ship Detection network,” 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China, 2020, pp. 669–673, https://doi.org/10.1109/ITAIC49862.2020.9338986.

30.

Moosbauer, S.; Konig, D.; Jakel, J.; Teutsch, M. A benchmark for deep learning based object detection in maritime environments. In Proceedings of the IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work (CVPRW), Long Beach, CA, USA, 16–17 June 2019; pp. 916–925.

31.

Spraul, R.; Sommer, L.; Arne, S. A comprehensive analysis of modern object detection methods for maritime vessel detection. Artificial Intelligence and Machine Learning in Defense Applications II. Int. Soc. Opt. Photonics 2020, 1154305.

32.

Betti, A.; Michelozzi, B.; Bracci, A.; Masini, A. Real-Time target detection in maritime scenarios based on YOLOv3 model. arXiv 2020, arXiv:2003.00800.

33.

Nalamati, M.; Sharma, N.; Saqib, M.; Blumenstein, M. Automated Monitoring in Maritime Video Surveillance System. In Proceedings of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), Wellington, New Zealand, 25–27 November 2020; pp. 1–6.

34.

Shao, Z.; Wu, W.; Wang, Z.; Du, W.; Li, C. SeaShips: A Large-Scale Precisely Annotated Dataset for Ship Detection. IEEE Trans. Multimed. 2018, 20, 2593–2604.

Titel: Marine Vision-Based Situational Automatic Ship Detection Using Remote Sensing Images
verfasst von: Anandakumar Haldorai
Babitha Lincy R
Suriya Murugan
Minu Balakrishnan
Verlag: Springer Nature Switzerland
Buch: Artificial Intelligence for Sustainable Development
Print ISBN: 978-3-031-53971-8

Electronic ISBN: 978-3-031-53972-5

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-3-031-53972-5_17

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