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Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance

Kyamelia Roy1, Sheli Sinha Chaudhuri1, Sayan Pramanik2, Soumen Banerjee2,*

1 Department of Electronics and Tele-Communication Engineering, Jadavpur University, Kolkata, 700032, India
2 Department of Electronics and Communication Engineering, University of Engineering and Management, Kolkata, 700160, India

* Corresponding Author: Soumen Banerjee. Email: email

Computer Systems Science and Engineering 2023, 44(1), 647-662. https://doi.org/10.32604/csse.2023.024997

Abstract

In recent years, computer vision finds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture. Automatic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies. Waterways being an important medium of transport require continuous monitoring for protection of national security. The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea. This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box technique. Furthermore, classified ships are again segmented with deep learning based auto-encoder model. The proposed model, in terms of classification, provides successful results generating 99.5% and 99.2% validation and training accuracy respectively. The auto-encoder model also produces 85.1% and 84.2% validation and training accuracies. Moreover the IoU metric of the segmented images is found to be of 0.77 value. The experimental results reveal that the model is accurate and can be implemented for automatic ship detection in water bodies considering remote sensing satellite images as input to the computer vision system.

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APA Style
Roy, K., Chaudhuri, S.S., Pramanik, S., Banerjee, S. (2023). Deep neural network based detection and segmentation of ships for maritime surveillance. Computer Systems Science and Engineering, 44(1), 647-662. https://doi.org/10.32604/csse.2023.024997
Vancouver Style
Roy K, Chaudhuri SS, Pramanik S, Banerjee S. Deep neural network based detection and segmentation of ships for maritime surveillance. Comput Syst Sci Eng. 2023;44(1):647-662 https://doi.org/10.32604/csse.2023.024997
IEEE Style
K. Roy, S.S. Chaudhuri, S. Pramanik, and S. Banerjee "Deep Neural Network Based Detection and Segmentation of Ships for Maritime Surveillance," Comput. Syst. Sci. Eng., vol. 44, no. 1, pp. 647-662. 2023. https://doi.org/10.32604/csse.2023.024997



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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