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

Computer Systems Science & Engineering 2023年第1期44卷 647-662页

作者： Kyamelia Roy Sheli Sinha Chaudhuri sayan pramanik Soumen Banerjee Department of Electronics and Tele-Communication Engineering Jadavpur UniversityKolkata700032India Department of Electronics and Communication Engineering University of Engineering and ManagementKolkata700160India

In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced ***-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water *** being an important medium of transport require continuous monitoring for protection of national *** 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 *** 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 ***,classified ships are again segmented with deep learning based auto-encoder *** proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy *** auto-encoder model also produces 85.1%and 84.2%validation and training *** the IoU metric of the segmented images is found to be of 0.77 *** experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.

关键词： Auto-encoder computer vision deep convolution neural network satellite imagery semantic segmentation ship detection

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