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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52270

Title: Ship detection based on YOLOv2 for SAR imagery
Authors: Yang-Lang Chang
Amare Anagaw
Lena Chang
Yi Chun Wang
Chih-Yu Hsiao
Wei-Hong Lee
Contributors: 國立臺灣海洋大學:通訊與導航工程學系
Keywords: synthetic aperture radar (SAR) images
ship detection
faster R-CNN
high performance computing
Date: 2019-03
Issue Date: 2019-06-21T02:29:58Z
Publisher: Remote Sensing
Abstract: Abstract: Synthetic aperture radar (SAR) imagery has been used as a promising data source formonitoring maritime activities, and its application for oil and ship detection has been the focus ofmany previous research studies. Many object detection methods ranging from traditional to deeplearning approaches have been proposed. However, majority of them are computationally intensiveand have accuracy problems. The huge volume of the remote sensing data also brings a challengefor real time object detection. To mitigate this problem a high performance computing (HPC) methodhas been proposed to accelerate SAR imagery analysis, utilizing the GPU based computing methods.In this paper, we propose an enhanced GPU based deep learning method to detect ship from theSAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposedto model the architecture and training the model. YOLOv2 is a state-of-the-art real-time objectdetection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) andSingle Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational timewith relatively competitive detection accuracy, we develop a new architecture with less number oflayers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detectiondataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets wereused for training and testing purposes. YOLOv2 test results showed an increase in accuracy of shipdetection as well as a noticeable reduction in computational time compared to Faster R-CNN. Fromthe experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecturehas a similarly competent detection performance as YOLOv2, but with less computational time on aNVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leapforward in improving the performance of SAR image ship detection.
Relation: 11(7) pp.786
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52270
Appears in Collections:[通訊與導航工程學系] 期刊論文

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