复杂场景下深度表示的SAR船舶目标检测算法

doi:10.3778/j.issn.1002-8331.2008-0117

摘要/Abstract

摘要： 雷达图像目标检测是国家海洋军事和经济发展的重点研究领域。与被动成像的光学雷达相比，合成孔径雷达（synthetic aperture radar，SAR）由于其高分辨率、全天候、全天时、主动式等特点，成为20世纪以来多国雷达研究的重要组成部分。图像目标检测是雷达图像解译的基础。提出一种复杂场景下深度表示的SAR船舶目标检测算法，针对SAR图像目标检测模型无法专注困难样本以及解决FPN多尺度金字塔融合的问题，提出将Libra R-CNN网络与NAS-FPN特征提取网络相结合。其中Libra R-CNN网络在采样、特征、目标三种水平分别具有先进的IoU平衡采样、平衡特征金字塔、平衡L1损失方法，同时将Libra R-CNN模型中的FPN特征提取网络替换为在COCO数据集中借助神经架构搜索（neural architecture search，NAS）方法形成的7层NAS-FPN网络。模型最终在SAR船舶数据集中进行了对比实验，与原先的NAS-FPN网络相比，组合后的网络平均精度提高了4.4个百分点，证明了模型融合后的有效性。

关键词: 合成孔径雷达（SAR）图像, 目标检测, Libra R-CNN网络, NAS-FPN网络

Abstract: Radar image target detection is a key research area of national marine military and economic development. Compared with passive imaging optical radar, due to synthetic aperture radar（SAR） has high resolution, all-weather, active and other characteristics, it has become an important part of radar research in many countries since the 20th century. Image target detection is the basis of radar image interpretation. This paper proposes a SAR ship target detection algorithm for depth representation in complex scenes. Because target detection model for SAR images cannot focus on difficult samples and solve the problem of FPN multi-scale pyramid fusion, it is proposed to combine the Libra R-CNN network with the NAS-FPN feature extraction network. Among them, the Libra R-CNN network has advanced IoU balanced sampling, balanced feature pyramid, and balanced L1 loss methods at the three levels of sampling, feature, and target, at the same time, the FPN feature extraction network in the Libra R-CNN model is replaced with a 7-layer NAS-FPN network formed by the neural architecture search（NAS） method at COCO data. The model is finally carried out in the SAR ship dataset in comparison experiments, compared with the original NAS-FPN network, the average accuracy of the combined network is improved by 4.4 percentage points, which proves the effectiveness of the model after fusion.

Key words: synthetic aperture radar（SAR） images, object detection, Libra R-CNN network, NAS-FPN network

袁国文, 张彩霞, 杨阳, 张文生, 白江波. 复杂场景下深度表示的SAR船舶目标检测算法[J]. 计算机工程与应用, 2022, 58(2): 289-294.

YUAN Guowen, ZHANG Caixia, YANG Yang, ZHANG Wensheng, BAI Jiangbo. SAR Target Detection Algorithm for Depth Representation in Complex Scenes[J]. Computer Engineering and Applications, 2022, 58(2): 289-294.

参考文献

[1] ZOU Z，SHI Z，GUO Y，et al.Object detection in 20 years：a survey[J].arXiv：1905.05055，2019.
[2] 杜臻.SAR图像舰船目标检测算法研究[D].哈尔滨：哈尔滨工业大学，2016.
DU Zhen.Research on the algorithm of ship target detection in SAR images[D].Harbin：Harbin Institute of Technology，2016.
[3] WU X，SAHOO D，HOI S C H.Recent advances in deep learning for object detection[J].Neurocomputing，2020，396：39-64.
[4] 史丹荣.基于深度学习的SAR图像舰船检测[D].西安：西安电子科技大学，2015.
SHI Danrong.SAR image ship detection based on deep learning[D].Xi’an：Xidian University，2015.
[5] 杜兰，刘彬，王燕，等.基于卷积神经网络的SAR图像目标检测算法[J].电子与信息学报，2016，38（12）：3018-3025.
DU Lan，LIU Bin，WANG Yan，et al.Target detection method based on convolutional neural network for SAR image[J].Journal of Electronics and Information Technology，2016，38（12）：3018-3025.
[6] 田壮壮，占荣辉，胡杰民，等.基于卷积神经网络的SAR图像目标识别研究[J].雷达学报，2016，5（3）：320-325.
TIAN Zhuangzhuang，ZHAN Ronghui，HU Jiemin，et al.SAR ATR based on convolutional neural network[J].Journal of Radars，2016，5（3）：320-325.
[7] 史鹤欢，许悦雷，马时平，等.PCA预训练的卷积神经网络目标识别算法[J].西安电子科技大学学报（自然科学版），2016，43（3）：161-166.
SHI Hehuan，XU Yuelei，MA Shiping，et al.Convolutional neural networks recognition algorithm based on PCA[J].Journal of Xidian University（Natural Science），2016，43（3）：161-166.
[8] 徐丰，王海鹏，金亚秋.深度学习在SAR目标识别与地物分类中的应用[J].雷达学报，2017，6（2）：136-148.
XU Feng，WANG Haipeng，JIN Yaqiu.Deep learning as applied in SAR target recognition and terrain classification[J].Journal of Radars，2017，6（2）：136-148.
[9] KANG M，LENG X，LIN Z，et al.A modified faster R-CNN based on CFAR algorithm for SAR ship detection[C]//2017 International Workshop on Remote Sensing with Intelligent Processing（RSIP），2017.
[10] 李君宝，杨文慧，许剑清，等.基于深度卷积网络的SAR图像目标检测识别[J].导航定位与授时，2017，4（1）：60-66.
LI Junbao，YANG Wenhui，XU Jianqing，et al.Deep convolutional network based SAR image object detection and recognition[J].Navigation Positioning and Timing，2017，4（1）：60-66.
[11] 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）：62-68.
LI Jianwei，QU Changwen，PENG Shujuan，et al.Ship detection in SAR images based on convolutional neural network[J].Systems Engineering and Electronics，2018，40（9）：62-68.
[12] BENTES C，VELOTTO D，TINGS B.Ship classification in TerraSAR-X images with convolutional neural networks[J].IEEE Journal of Oceanic Engineering，2018，43（1）：258-266.
[13] GHIASI G，LIN T Y，LE Q V.NAS-FPN：learning scalable feature pyramid architecture for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2019：7036-7045.
[14] PANG J，CHEN K，SHI J，et al.Libra R-CNN：towards balanced learning for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2019：821-830.
[15] FUKUSHIMA K.Neocognitron：a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J].Biological Cybernetics，1980，36（4）：193-202.
[16] LIN T Y，DOLLáR P，GIRSHICK R，et al.Feature pyramid networks for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition，2017：2117-2125.
[17] GIRSHICK R.Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision，2015：1440-1448.
[18] REN S，HE K，GIRSHICK R，et al.Faster R-CNN：towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems，2015：91-99.
[19] REAL E，AGGARWAL A，HUANG Y，et al.Regularized evolution for image classifier architecture search[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019，33：4780-4789.
[20] WANG Y，WANG C，ZHANG H，et al.A SAR dataset of ship detection for deep learning under complex backgrounds[J].Remote Sensing，2019，11（7）：765.
[21] CHEN K，WANG J，PANG J，et al.MMDetection：open mmlab detection toolbox and benchmark[J].arXiv：1906.
07155，2019.