基于生成式对抗网络的合成孔径雷达舰船数据增广在改进单次多盒检测器中的应用

doi:10.3969/j.issn.1000-1093.2019.12.013

摘要/Abstract

摘要： 针对合成孔径雷达（SAR）图像舰船目标检测领域舰船数据获取成本较高、数据集稀少的问题，提出一种基于像素对像素（pix2pix）生成式对抗网络(GAN)的数据增广技术。制作一个用于pix2pix GAN的数据集，通过对GAN网络的训练和测试得到800张新的SAR舰船样本，并对生成的典型样本进行了客观评价；针对传统SAR舰船目标检测算法鲁棒性差、易受斑点噪声影响的缺点，提出一种基于改进单次多盒检测器(SSD)的SAR舰船检测算法，通过在SSD加入Inception模块增强其对多尺寸目标适应性,提高检测器性能；将pix2pix GAN生成的SAR舰船数据进行标注后加入改进的SSD中，在SAR舰船检测数据集上进行大量对比实验。实验结果表明：当将生成的样本加入原SSD后，检测精度比原SSD检测算法提高了4.3%；当将生成的样本加入改进的SSD后，检测精度相比改进的SSD提高了1.9%；检测器中没有加入生成样本的情况下，改进SSD算法相比原SSD检测算法，检测精度提升了4.7%.

关键词: 舰船, 合成孔径雷达, 目标检测, 像素对像素, 生成式对抗网络, 单次多盒检测器

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

中图分类号:

TP751.1

杨龙，苏娟，李响. 基于生成式对抗网络的合成孔径雷达舰船数据增广在改进单次多盒检测器中的应用[J]. 兵工学报, 2019, 40(12): 2488-2496.

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.

参考文献

［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]	李博，王博，韩京冶，杨宗睿，栗霖雲，傅畅之. 基于车载计算机的红外图像移动目标检测[J]. 兵工学报, 2022, 43(S1): 66-73.
[2]	徐熙毅，谭鸽伟，李彪. 基于空变分离的两步聚焦双基曲线合成孔径雷达成像[J]. 兵工学报, 2022, 43(6): 1365-1375.
[3]	王少博，张成，苏迪，冀瑞静. 基于改进YOLOv3和核相关滤波算法的旋转弹目标探测算法[J]. 兵工学报, 2022, 43(5): 1032-1045.
[4]	王锴松，周国华，刘月林，王玉芬，刘胜道. 地磁模拟法测量舰船感应磁场的数值模拟[J]. 兵工学报, 2022, 43(3): 617-625.
[5]	于博文，吕明. 改进的YOLOv3算法及其在军事目标检测中的应用[J]. 兵工学报, 2022, 43(2): 345-354.
[6]	郭成豹，胡松，王文井，殷琦琦. 利用磁传感器阵列磁场差值的舰船磁场反演建模方法[J]. 兵工学报, 2022, 43(1): 111-119.
[7]	徐英，谷雨，彭冬亮，刘俊，陈华杰. 面向合成孔径雷达图像任意方向舰船检测的改进YOLOv3模型[J]. 兵工学报, 2021, 42(8): 1698-1707.
[8]	喻鹏，程锦房，张伽伟，姜润翔. 基于Rao检测器的舰船轴频电场滑动门限检测方法[J]. 兵工学报, 2021, 42(4): 827-834.
[9]	张永梅，赖裕平，马健喆，冯超，束颉. 基于视频的装甲车和飞机检测跟踪及轨迹预测算法[J]. 兵工学报, 2021, 42(3): 545-554.
[10]	马月红，孔梦瑶. 基于改进快速区域卷积网络的目标检测轻量化算法[J]. 兵工学报, 2021, 42(12): 2664-2674.
[11]	王逸南，张建伟，王治，姚熊亮，杨娜娜. 基于板厚补偿的921A钢与Q345钢靶板在截卵形弹体侵彻下的等效方法[J]. 兵工学报, 2021, 42(11): 2465-2475.
[12]	戴文君，常天庆，褚凯轩，张雷，郭理彬. 基于时空卷积特征记忆模型的坦克火控系统视频目标检测方法[J]. 兵工学报, 2020, 41(9): 1708-1718.
[13]	王健，秦春霞，杨珂，任萍. 基于导向重构与降噪稀疏自编码器的合成孔径雷达目标识别[J]. 兵工学报, 2020, 41(9): 1861-1870.
[14]	王成，吴岩，杨廷飞. 利用改进单分类支持向量机提升舰船尾流目标的检测准确率[J]. 兵工学报, 2020, 41(9): 1887-1893.
[15]	李响，苏娟，杨龙. 基于改进YOLOv3的合成孔径雷达图像中建筑物检测算法[J]. 兵工学报, 2020, 41(7): 1347-1359.