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

doi:10.3969/j.issn.1000-1093.2019.12.013

Abstract

Abstract: 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.Key

Key words: ship, syntheticapertureradar, objectdetection, pix2pix, generativeadversarialnetwork, singleshotmultiboxdetector

CLC Number:

TP751.1

YANG Long， SU Juan， LI Xiang. Application of SAR Ship Data Augmentation Based on Generative Adversarial Network in Improved SSD[J]. Acta Armamentarii, 2019, 40(12): 2488-2496.

References

［1］ ERHAN D, SZEGEDY C, TOSHEV A, et al. Scalable object detection using deep neural networks［C］∥ Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, US: IEEE, 2014: 2147-2154.
［2］李健伟, 曲长文, 彭书娟, 等. 基于卷积神经网络的SAR图像舰船目标检测［J］. 系统工程与电子技术, 2018, 40(9): 1953-1959.
LI J W, QU C W, PENG S J, et al. Ship detection in SAR images based on convolutional neural network ［J］. Systems Engineering and Electronics, 2018, 40(9): 1953-1959.
(in Chinese)
［3］ REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks ［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015, 39(6): 1137-1149.
［4］李健伟，曲长文，彭书娟，等. 基于生成对抗网络和线上难例挖掘的SAR图像舰船目标检测［J］. 电子与信息学报, 2019, 41(1): 143-149.
LI J W, QU C W, PENG S J, et al.Ship detection in SAR images based on generative adversarial network and online hard examples mining［J］.Journal of Electronics and Information Technology, 2019, 41(1): 143-149.(in Chinese)
［5］ GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Ge-nerative adversarial nets［C］∥Proceedings of Advances in Neural Information Processing Systems. Montreal, Canada: NIPS Foundation, 2014: 2672-2680.
［6］ GIRSHICK R. Fast R-CNN［C］∥Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1440-1448.
［7］ KANG M, JI K F, LENG X G, et al. Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection ［J］. Remote Sensing, 2017, 9(8): 1-14.
［8］ JIAO J, ZHANG Y, SUN H, et al. A densely connected end-to-end neural network for multiscale and multiscene SAR ship detection ［J］. IEEE Access, 2018, 6: 20881-20892.
［9］ RADFORD A, METZ L, CHINTALA S. Unsupervised representation learning with deep convolutional generative adversarial networks［C］∥Proceedings of the 4th International Conference on Learning Representation. San Juan, Puerto Rico: ［s.n.］, 2016.

［10］ CHEN X, DUAN Y, HOUTHOOFT R, et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets［C］∥Proceedings of Advances in Neural Information Processing Systems. Barcelona, Spain: NIPS Foundation, 2016: 2172-2180.
［11］ ISOLA P, ZHU J Y, ZHOU T, et al. Image-to-image translation with conditional adversarial networks［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, US: IEEE, 2017: 1125-1134.
［12］ LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector［C］∥Proceedings of European Conference on Computer Vision. Amsterdam, the Netherlands: Springer, 2016: 21-37.
［13］ SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Boston, MA,US:IEEE, 2015: 1-9.
［14］ SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Lasvegas, NE, US: IEEE, 2016: 2818-2826.
［15］ BAO P, ZHANG L, WU X L. Canny edge detection enhancement by scale multiplication ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(9): 1485-1490.
［16］ JOHNSON J, ALAHI A, FEI-FEI L. Perceptual losses for real-time style transfer and super-resolution［C］∥Proceedings of European Conference on Computer Vision. Amsterdam, the Netherlands: Springer, 2016: 694-711.
［17］ YANG J, PRICE B, COHEN S, et al. Object contour detection with a fully convolutional encoder-decoder network［C］∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Lasvegas, NE, US: IEEE, 2016: 193-202.
［18］句彦伟,张燕.ISAR图像质量评估方法［J］.系统工程与电子技术,2015,37(2):297-303.
JU Y W, ZHANG Y. Research on ISAR image quality evaluation［J］. Systems Engineering and Electronics, 2015, 37(2): 297- 303. (in Chinese)
［19］ EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The PASCAL visual object classes (VOC) challenge ［J］. International Journal of Computer Vision, 2010, 88(2): 303-338.
［20］ IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift［C］∥ Proceedings of International Conference on Machine Learning. Lille, France: The International Machine Learning Society,2015.
［21］ BOTTOU L. Large-scale machine learning with stochastic gradient descent［C］∥Proceedings of the 19th International Conference on Computational Statistics. Paris, France: Physica-Verlag HD, 2010: 177-186.
［22］ KINGMA D P, BA J. Adam: a method for stochastic optimization［C］∥Proceedings of the 3rd International Conference on Learning Representations. San Diego, CA, US: ［s.n.］, 2015.
［23］ SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al. Dropout: a simple way to prevent neural networks from overfitting［J］. The Journal of Machine Learning Research, 2014, 15(1): 1929-1958.

第40卷
第12期2019 年12月兵工学报ACTA
ARMAMENTARIIVol.40No.12Dec.2019

[1]	LI Bo， WANG Bo， HAN Jingye， YANG Zongrui， LI Linyun， FU Changzhi. Infrared Image Moving Object Detection Technology Based on Onboard Computer [J]. Acta Armamentarii, 2022, 43(S1): 66-73.
[2]	XU Xiyi， TAN Gewei， LI Biao. Two-step Imaging of Bistatic SAR with Curvilinear Trajectory Based on Space-Variant Separation [J]. Acta Armamentarii, 2022, 43(6): 1365-1375.
[3]	DENG Hui， WANG Erli， YI Wenbin， WANG Kebin， ZHANG Zhihong. Numerical Models for Calculating Ship Hydrodynamic Pressure Field in Restricted Waters with Significant Blocking Effect [J]. Acta Armamentarii, 2022, 43(3): 605-616.
[4]	WANG Kaisong， ZHOU Guohua， LIU Yuelin， WANG Yufen， LIU Shengdao. Numerical Simulation of Measuring Ship's Induced Magnetic Field by Geomagnetic Field Simulation Method [J]. Acta Armamentarii, 2022, 43(3): 617-625.
[5]	LI Jinqing, LIU Zefei, MAN Zhenlong. Key Generation Method Based on Generative Adversarial Network and Its Application in Low-light-level Image Encryption [J]. Acta Armamentarii, 2022, 43(2): 337-344.
[6]	MENG Guanglei, LI Shufa, LIU Binbin, ZHOU Mingzhe, SUN Donglai, WU Hao. Self-learning Fuzzy Grey Method for Plateau Environmental Adaptability Assessment of Air Defense Early-warning Radar [J]. Acta Armamentarii, 2022, 43(1): 98-110.
[7]	GUO Chengbao， HU Song， WANG Wenjing， YIN Qiqi. Ship Magnetic Field Inversion Modeling Method Utilizing the Magnetic Field Difference between Magnetic Sensors [J]. Acta Armamentarii, 2022, 43(1): 111-119.
[8]	XU Ying， GU Yu， PENG Dongliang， LIU Jun， CHEN Huajie. An Improved YOLOv3 Model for Arbitrary-oriented Ship Detection in SAR Image [J]. Acta Armamentarii, 2021, 42(8): 1698-1707.
[9]	YU Peng， CHENG Jinfang， ZHANG Jiawei， JIANG Runxiang. Ship Shaft-rate Electric Field Sliding Threshold Detection Method Based on Rao Detector [J]. Acta Armamentarii, 2021, 42(4): 827-834.
[10]	SONG Guibao， JIANG Zijie， LIU Zhan， LIU Zhenyu. Optimization Model of Multi-type Shore-to-ship Missile Combat Unit Deployment [J]. Acta Armamentarii, 2021, 42(4): 878-887.
[11]	MA Yuehong， KONG Mengyao. A Lightweight Target Detection Algorithm Based on the Improved Faster-RCNN [J]. Acta Armamentarii, 2021, 42(12): 2664-2674.
[12]	XUE Lingzhi, ZENG Xiangyang, YANG Shuang. Underwater Acoustic Target Recognition Algorithm Based on Generative Adversarial Networks [J]. Acta Armamentarii, 2021, 42(11): 2444-2452.
[13]	WANG Yinan， ZHANG Jianwei， WANG Zhi， YAO Xiongliang， YANG Nana. Equivalent Method for 921A Steel and Q345 Steel Target Plates Based on Plate Thickness Compensation Model under thePenetration of Truncated-oval Nose Projectile [J]. Acta Armamentarii, 2021, 42(11): 2465-2475.
[14]	DAI Wenjun， CHANG Tianqing， CHU Kaixuan， ZHANG Lei， GUO Libin. Video Object Detection Method for Tank Fire Control System Based on Spatial-temporal Convolution Feature Memory Model [J]. Acta Armamentarii, 2020, 41(9): 1708-1718.
[15]	WANG Jian， QIN Chunxia， YANG Ke， REN Ping. A SAR Target Recognition Algorithm Based on Guided Filter Reconstruction and Denoising Sparse Autoencoder [J]. Acta Armamentarii, 2020, 41(9): 1861-1870.