Object Detection in Remote Sensing Images Based on Improved SSD Algorithm

doi:10.19678/j.issn.1000-3428.0058660

Abstract

Abstract: In the field of object detection in remote sensing images, most of the existing object detection algorithms perform poorly for small objects.This paper proposes an algorithm that fuses multi-scale features for object detection in remote sensing images.The features are first extracted by using the basic network of the SSD algorithm to form a feature map pyramid.Then the feature map fusion module is designed to fuse the position information of the shallow feature map and the semantic information of the deep feature map, retaining rich context information.Finally, a module to remove redundant information is designed, and the features in the feature map are further extracted through the convolution operation.The feature information is also screened to reduce the aliasing effect brought by the fusion of the feature maps.The experimental results on NWPU VHR-10, a dataset of remote sensing images, show that the proposed algorithm achieves an average detection accuracy of 93.9%, demonstrating that it outperforms Faster R-CNN, SSD and other algorithms in detection of small objects in remote sensing images.

Key words: remote sensing image, object detection, feature fusion, Convolutional Neural Network(CNN), deconvolution

摘要： 在遥感图像目标检测领域，多数目标检测算法针对小目标检测时效果不佳，为此，提出一种多尺度特征融合的遥感图像目标检测算法。利用SSD算法的基础网络进行特征提取，形成特征图金字塔。设计特征图融合模块，融合浅层特征图的位置信息和深层特征图的语义信息，从而保留丰富的上下文信息。设计冗余信息去除模块，通过卷积操作进一步提取特征图中的特征，并对特征信息进行筛选，以减少特征图融合时带来的混叠效应。在遥感图像数据集NWPU VHR-10上的实验结果表明，该算法的平均检测精度高达93.9%，其针对遥感图像小目标的检测性能优于Faster R-CNN和SSD等算法。

关键词: 遥感图像, 目标检测, 特征融合, 卷积神经网络, 反卷积

CLC Number:

TP18

ZHANG Yan, DU Huijuan, SUN Yemei, LI Xianguo. Object Detection in Remote Sensing Images Based on Improved SSD Algorithm[J]. Computer Engineering, 2021, 47(9): 252-258,265.

张艳, 杜会娟, 孙叶美, 李现国. 基于改进SSD算法的遥感图像目标检测[J]. 计算机工程, 2021, 47(9): 252-258,265.

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References

[1] CHENG G, HAN J W.A survey on object detection in optical remote sensing images[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 117:11-28.
[2] DENG A P, SUN H, ZHOU S L, et al.Multi-scale object detection in remote sensing imagery with convolutional neural networks[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 145:3-22.
[3] 董超, 刘晶红, 徐芳, 等.光学遥感图像舰船目标快速检测方法[J].吉林大学学报(工学版), 2019, 49(4):1369-1376. DONG C, LIU J H, XU F, et al.Fast ship detection in optical remote sensing images[J].Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4):1369-1376.(in Chinese)
[4] 王广学, 黄晓涛, 周智敏.基于邻域统计分布变化分析的UWB SAR隐蔽目标变化检测[J].电子与信息学报, 2011, 33(1):49-54. WANG G X, HUANG X T, ZHOU Z M.UWB SAR hidden object change detection based on analysis of neighborhood statistical distribution change[J].Journal of Electronics and Information, 2011, 33(1):49-54.(in Chinese)
[5] 吴畏.基于知识的目标识别与跟踪技术研究[D].哈尔滨:哈尔滨工业大学, 2007. WU W.Research on knowledge-based object recognition and tracking technology[D].Harbin:Harbin Institute of Technology, 2017.(in Chinese)
[6] 曹家梓, 宋爱国.基于马尔科夫随机场的纹理图像分割方法研究[J].仪器仪表学报, 2015, 36(4):776-786. CAO J X, SONG A G.Research on texture image segmentation method based on Markov random field[J].Journal of Instrumentation, 2015, 36(4):776-786.(in Chinese)
[7] GIRSHICK R.Fast R-CNN[C]//Proceedings of IEEE International Conference on Computer Vision.Washington D.C., USA:IEEE Press, 2015:1440-1448.
[8] REN S Q, HE K M, GIRSHICK R, et al.Faster R-CNN:towards real-time object detection with region proposal networks[C]//Proceedings of IEEE Conference on Neural Information Processing Systems.Washington D.C., USA:IEEE Press, 2015:91-99.
[9] REDMON J, DIVVALA S, GIRSHICK R, et al.You only look once:unified, real-time object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2016:779-788.
[10] LIU W, ANGUELOV D, ERHAN D, et al.SSD:single shot multi box detector[C]//Proceedings of European Conference on Computer Vision.Berlin, Germany:Springer, 2016:21-37.
[11] REDMON J, FARHADI A.YOLO9000:better, faster, stronger[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:6517-6525.
[12] REDMON J, FARHADI A.YOLOv3:an incremental improvement[EB/OL].[2020-05-05].https://pjreddie.com/media/files/papers/YOLOv3.pdf.
[13] FU C Y, LIU W, RANGA A, et al.DSSD:deconvolutional single shot detector[EB/OL].[2020-05-05].https://export.arxiv.org/pdf/1701.06659.
[14] CAO G M, XIE X M, YANG W Z, et al.Feature-fused SSD:fast detection for small objects[EB/OL].[2020-05-05].https://export.arxiv.org/vc/arxiv/papers/1709/1709.05054v1.pdf.
[15] YAO Y, JIANG Z G, ZHANG H P, et al.Ship detection in optical remote sensing images based on deep convolutional neural networks[J].Journal of Applied Remote Sensing, 2017, 11(4):042611.
[16] 辛鹏, 许悦雷, 唐红, 等.全卷积网络多层特征融合的飞机快速检测[J].光学学报, 2018, 38(3):337-343. XIN P, XU Y L, TANG H, et al.Fast aircraft detection based on multi-layer feature fusion of fully convolutional network[J].Journal of Optics, 2018, 38(3):337-343.(in Chinese)
[17] 赵丹新.遥感图像中飞机目标提取的深度学习方法研究[D].上海:中国科学院上海技术物理研究所, 2018. ZHAO D X.Research on deep learning method of aircraft object extraction in remote sensing images[D].Shanghai:Shanghai Institute of Technical Physics, Chinese Academy of Sciences, 2018.(in Chinese)
[18] 李文斌, 何冉.基于深度神经网络的遥感图像飞机目标检测[J].计算机工程, 2020, 46(7):268-276. LI W B, HE R.Aircraft target detection of remote sensing images based on deep neural network[J].Computer Engineering, 2020, 46(7):268-276.(in Chinese)
[19] VAN E A.You only look twice:rapid multi-scale object detection in satellite imagery[EB/OL].[2020-05-05].http://arxiv-export-lb.library.cornell.edu/pdf/1805. 09512.
[20] REN Y, ZHU C R, XIAO S P.Small object detection in optical remote sensing images via modified Faster R-CNN[J].Applied Sciences, 2018, 8(5):813-824.
[21] PANG J M, LI C, SHI J P, et al.R2-CNN:fast tiny object detection in large-scale remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing, 2019, 57:5512-5524.
[22] BAI Y C, ZHANG Y Q, DING M L, et al.SOD-MTGAN:small object detection via multi-task generative adversarial network[C]//Proceedings of European Conference on Computer Vision(ECCV).Berlin, Germany:Springer, 2018:206-221.
[23] DONG R C, XU D A, ZHAO J, et al.Sig-NMS-based Faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery[J].IEEE Transactions on Geoscience and Remote Sensing.2019, 57:8534-8545.
[24] JAKARIA R, NILANJAN R, SUBIR C, et al.Small-object detection in remote sensing images with end-to-end edge-enhanced GAN and object detector network[J].Remote Sensing, 2020, 12(9):1432-1435.
[25] 王俊强, 李建胜, 周学文, 等.改进的SSD算法及其对遥感影像小目标检测性能的分析[J].光学学报, 2019, 39(6):373-382. WANG J Q, LI J S, ZHOU X W, et al.Improved SSD algorithm and performance analysis of small object detection in remote sensing images[J].Journal of Optics, 2019, 39(6):373-382.(in Chinese)
[26] HUANG G, LIU Z, VAN DER MAATEN L, et al.Densely connected convolutional networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:2261-2269.
[27] LIN T, DOLLÁR P, GIRSHICK R, et al.Feature pyramid networks for object detection[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2017:936-944.
[28] SUN K, XIAO B, LIU D, et al.Deep high-resolution representation learning for human pose estimation[C]//Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition.Washington D.C., USA:IEEE Press, 2019:5693-5703.
[29] LI K, ZOU C Q, BU S H, et al.Multi-modal feature fusion for geographic image annotation[J].Pattern Recognition, 2018, 73:1-14.
[30] CHENG G, ZHOU P C, HAN J W.Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12):7405-7415.
[31] HE K M, GKIOXARI G, DOLLÁR P, et al.Mask R-CNN[EB/OL].[2020-05-05].https://web.cs.ucdavis.edu/~yjlee/teaching/ecs289g-winter2018/Mask_RCNN.pdf.

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