Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 176-180.doi: 10.11896/JsJkx.191100206

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Remote Sensing Image ObJect Detection Technology Based on Improved YOLO-V2 Algorithm

ZHANG Man, LI Jie, DING Rong-li, CHENG Hao-tian and SHEN Ji   

  1. Shanghai Aerospace Electronics Technology Research Institute,Shanghai 201109,China
  • Published:2020-07-07
  • About author:ZHANG Man, born in 1993, master’s degree.Her main research interests include image processing and remote sensing application.

Abstract: Traditional method of remote sensing image obJect detection has the disadvantages of high time complexity and low precision.How to detect specific targets in remote sensing images quickly and accurately has become a hot research topic.In order to solve this problem,this paper improves the YOLO-V2 obJect detection algorithm,reduces the convolution layers and dimension,and combined with the ideal of feature pyramid to increase the detection features’ scale,so as to achieve the purpose of improving detection accuracy.At the same time,a general processing framework of remote sensing image obJect detection algorithm based on deep learning is presented to solve the problem that large remote sensing images cannot be directly processed.Comparison experiments on the DOTA dataset show that the improved YOLO-V2 algorithm has better accuracy and recall rate in 15 categories than the YOLO-V2 algorithm,and the mAP value is increased by 0.12.In terms of time complexity,it is slightly lower than the YOLO-V2 algorithm.Specifically,on 416×416 image patches,the detection time of the improved YOLO-V2 algorithm is reduced by 0.1 ms compared to the YOLO-V2 algorithm.

Key words: Deep learning, Remote sensing image, ObJect detection, YOLO-V2, Feature pyramid, Multiscale

CLC Number: 

  • TP751
[1] CAO Q,ZHENG H,LI X S.A Cloud Detection Method for Sate-llite Remote Sensing Image Based on Texture Features.Journal of Aeronautics,2007,28(3):661-666.
[2] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate obJect detection and semantic segmentation//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2014:580-587.
[3] UIJLINGS J R R,VAN DE SANDE K E A,GEVERS T,et al.Selective search for obJect recognition.International Journal of Computer Vision,2013,104(2):154-171.
[4] GIRSHICK R.Fast r-cnn.arXiv:1504.08083,2015.
[5] REN S,HE K,GIRSHICK R,et al.Faster r-cnn:Towards real-time obJect detection with region proposal networks//Advances In Neural Information Processing Systems.2015:91-99.
[6] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time obJect detect//Las Vegas:Proceeding of the IEEE Conference on Computer Vision and Pattern Recogniton.IEEE,2016.
[7] REDMOD J,FARHADI A.YOLO9000:Better,Faster,Stronger//2017 IEEE Conference on Computer Vision and Pattern Recognition,Honolulu.IEEE,2017:6517-6525.
[8] SEFERBEKOV S S,LGLOVIKOV V I,et al.Feature Pyramid Network for Multi-Class land Segmentation//IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.IEEE,2018.
[9] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition.arXiv:1409.1556,2014.
[10] LIN M,CHEN Q,YAN S.Network in network.arXiv:1312.4400,2013.
[11] LOFE S,SZEGEDY C.Batch Normalization:Accelerating Deep Network Training by Reducing Internal Covariate Shift//International Conference on Machine Learning.2015.
[12] HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016:770-778.
[13] ZHANG S J,ZHAO H C.Algorithm research of optimal cluster number and initial cluster center.Application Research of Computers,2017,34(6):1617-1620.
[14] ROTHE R,GUILLAUMIN M,VAN GOOL L.Non-maximum suppression for obJect detection by passing messages between windows//Asian Conference on Computer Vision.Springer,Cham,2014:290-306.
[15] VAN ETTEN A.You Only Look Twice:Rapid Multi-Scale ObJect Detection in Satellite Imagery//IEEE Conference on Computer Vision and Pattern Recognition.2018.
[16] XIA G S,BAI X,et al.DOTA:A Large-scale Dataset for ObJect Detection in Aerial Images//IEEE Conference on Computer Vision and Pattern Recogniton.2018.
[17] QIAN N.On the momentum term in gradient descent learning algorithms.Neural Networks,1999,12(1):145-151.
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