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Open AccessArticle

Ship Detection Based on YOLOv2 for SAR Imagery

1
Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan
2
Department of Communications and Guidance Engineering, National Taiwan Ocean University, Keelung 20248, Taiwan
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 786; https://doi.org/10.3390/rs11070786
Received: 2 February 2019 / Revised: 18 March 2019 / Accepted: 27 March 2019 / Published: 2 April 2019
(This article belongs to the Special Issue GPU Computing for Geoscience and Remote Sensing)
Synthetic aperture radar (SAR) imagery has been used as a promising data source for monitoring maritime activities, and its application for oil and ship detection has been the focus of many previous research studies. Many object detection methods ranging from traditional to deep learning approaches have been proposed. However, majority of them are computationally intensive and have accuracy problems. The huge volume of the remote sensing data also brings a challenge for real time object detection. To mitigate this problem a high performance computing (HPC) method has 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 the SAR images. The You Only Look Once version 2 (YOLOv2) deep learning framework is proposed to model the architecture and training the model. YOLOv2 is a state-of-the-art real-time object detection system, which outperforms Faster Region-Based Convolutional Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) methods. Additionally, in order to reduce computational time with relatively competitive detection accuracy, we develop a new architecture with less number of layers called YOLOv2-reduced. In the experiment, we use two types of datasets: A SAR ship detection dataset (SSDD) dataset and a Diversified SAR Ship Detection Dataset (DSSDD). These two datasets were used for training and testing purposes. YOLOv2 test results showed an increase in accuracy of ship detection as well as a noticeable reduction in computational time compared to Faster R-CNN. From the experimental results, the proposed YOLOv2 architecture achieves an accuracy of 90.05% and 89.13% on the SSDD and DSSDD datasets respectively. The proposed YOLOv2-reduced architecture has a similarly competent detection performance as YOLOv2, but with less computational time on a NVIDIA TITAN X GPU. The experimental results shows that the deep learning can make a big leap forward in improving the performance of SAR image ship detection. View Full-Text
Keywords: synthetic aperture radar (SAR) images; ship detection; YOLOv2; faster R-CNN; YOLOv2-reduced; high performance computing synthetic aperture radar (SAR) images; ship detection; YOLOv2; faster R-CNN; YOLOv2-reduced; high performance computing
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Chang, Y.-L.; Anagaw, A.; Chang, L.; Wang, Y.C.; Hsiao, C.-Y.; Lee, W.-H. Ship Detection Based on YOLOv2 for SAR Imagery. Remote Sens. 2019, 11, 786.

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