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Title: Ship detection using SAR images based on YOLO (you only look once)
Authors: Melillos, George 
Kalogirou, Eleftheria 
Makri, Despina 
Hadjimitsis, Diofantos G. 
Major Field of Science: Engineering and Technology
Field Category: Computer and Information Sciences;ENGINEERING AND TECHNOLOGY
Keywords: YOLO;Remote Sensing;Sentinel-1;Ship detection
Issue Date: 21-Sep-2023
Project: EXCELSIOR: ERATOSTHENES Centre of Excellence for Earth Surveillance and Space-Based Monitoring of the Environment : Teaming Phase1 GA 763643 
Conference: Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023), 2023, Ayia Napa, Cyprus 
Abstract: This paper proposes an automatic ship detection approach in Synthetic Aperture Radar (SAR) Images using YOLO deep learning framework. YOLO (You Only Look Once) is an object detection algorithm Object detection algorithms using region proposal includes RCNN, Fast RCNN, and Faster RCNN, etc. Region based Convolutional Neural Networks (RCNN) algorithm uses a group of boxes for the image and then analyses in each box if either of the boxes holds a target. It employs the method of selective search to pick those sections from the picture. YOLO can be used to assist in making safety checks for ships and mariners. We train the YOLO model on our dataset in this paper for our detector to learn to detect objects in SAR images such as ships. The preliminary YOLO test results showed an increase in the accuracy of ship detection at Cyprus’s Coast and can be applied in the field of ship detection.
Type: Conference Papers
Affiliation : ERATOSTHENES Centre of Excellence 
Cyprus University of Technology 
Appears in Collections:EXCELSIOR H2020 Teaming Project Publications

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