Application of SAR Ship Data Augmentation Based on Generative Adversarial Network in Improved SSD

被引：0
|
作者：
Yang L. [1 ]
Su J. [1 ]
Li X. [1 ]
机构：
[1] College of Nuclear Engineering, Rocket Force University of Engineering, Xi'an, 710025, Shaanxi
来源：
Binggong Xuebao/Acta Armamentarii | 2019年 / 40卷 / 12期
关键词：
Generative adversarial network; Object detection; Pix2pix; Ship; Single shot multibox detector; Synthetic aperture radar;
D O I：
10.3969/j.issn.1000-1093.2019.12.013
中图分类号：
学科分类号：
摘要：
For the high cost of data acquisition and few of datasets for ship detection in synthetic aperture radar (SAR) image, a data augmentation technology based on pix2pix is proposed. A dataset is set for pix2pix, and 800 SAR ship samples are obtained by training and testing the generative adversarial network (GAN). The objective evaluation is given for the generated typical samples. And for the problems that the accuracy of traditional ship detection in SAR images is susceptible to speckle noise and its generalization is poor, a ship detection algorithm based on single shot multibox detector (SSD) is proposed. An Inception module is added into the SSD detecting algorithm for enhancing its adaptability to multi-size target and improving the performance of detector. Finally, the SAR ship data generated by pix2pix GAN is marked and added to the improved SSD. A large number of comparison experiments were performed on the SSDD dataset. The experimental results show that the detection accuracy is improved by 4.3% when the generated samples are added to SSD. The detection accuracy is improved by 1.9% after the samples are added to the improved SSD; and the detection accuracy of the improved SSD is improved by 4.7% without the addition of the generated sample in the detector. © 2019, Editorial Board of Acta Armamentarii. All right reserved.
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页码：2488 / 2496
页数：8
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