

浏览全部资源
扫码关注微信
新疆大学软件学院
Published:2020
移动端阅览
[1]韩文轩,阿里甫·库尔班,黄梓桐.基于改进SSD算法的遥感影像小目标快速检测[J].新疆大学学报(自然科学版)(中英文),2020,37(02):163-169.
[1]韩文轩,阿里甫·库尔班,黄梓桐.基于改进SSD算法的遥感影像小目标快速检测[J].新疆大学学报(自然科学版)(中英文),2020,37(02):163-169. DOI: 10.13568/j.cnki.651094.651316.2019.11.18.0002.
DOI:10.13568/j.cnki.651094.651316.2019.11.18.0002.
针对遥感影像小目标检测难度大、准确率低、耗时长等问题
本文提出一种基于改进SSD算法以提升遥感影像小目标实时检测精度的方法.(1)采用深度可分离卷积代替普通卷积层
从而减少计算量、加快目标检测速度;(2)修改SSD网络层数
最终使用7个卷积层的SSD作为检测器
选取其中4个卷积层的输出进行检测
进一步减少模型复杂度和训练难度;(3)修改了每个检测层所产生的候选框大小
提高检测精度.实验结果表明:所提出的模型平均准确率达到82.40%
平均每张影像检测耗时1.86 s
充分验证了该方法的有效性.本文提出的基于改进的SSD算法在遥感影像小目标检测中具备有效性和实时性
在遥感影像小目标检测任务中效果良好.
Aiming at the difficulty
accuracy and time consumption of small object detection in remote sensing image
a real-time detection method of small object in remote sensing image based on an improved SSD algorithm was proposed.(1) the depth-separable convolution is adopted to replace the ordinary convolution layer
so as to reduce the computation and speed up the target detection.(2) carefully modify the number of SSD network layers
finally use SSD of 7 convolutional layers as the detector
and select the output of 4 convolutional layers for detection
further reducing the complexity of the model and the difficulty of training.(3) carefully modified the anchor frame size and length-width ratio of each detection layer to improve the detection accuracy.The average accuracy of the proposed model reached 82.40%
and the average detection time per image was 1.86 s
which fully verified the effectiveness of the method.The proposed method in this paper is effective and real-time
and it has a good effect on the task of detecting small objects in remote sensing images.
ZHANG S,HE G,CHEN H B,et al.Scale adaptive proposal network for object detection in remote sensing images[J].IEEE Geoscience and Remote Sensing Letters,2019:1-5.
CAO X,WU C,YAN P,et al.Linear SVM classification using boosting HOG features for vehicle detection in low-altitude airborne videos[C]//2011 18th IEEE International Conference on Image Processing(ICIP).Brussels:IEEE,2011:2421-2424.
LOWE D G.Object recognition from local scale-invariant features[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision.Kerkyra,Greece:IEEE,1999:1150.
[41 HE D C,WANG L.Texture unit,texture spectrum and texture analysis[J].IEEE Transactions on Geoscience&Remote Sensing,1990,28(4):509-512.
HE K,ZHANG X,REN S,et al.Spatial pyramid pooling in deep convolutional networks for visual recognition[C]//In European Conference on Computer Vision,Proceedings of the 13th European Conference,Zurich,Switzerland,6-12 September 2014;Springer:Cham,Switzerland,2014:346-361.
GIRSHICK R.Fast R-CNN[C]//In Proceedings of the IEEE International Conference on Computer Vision,Santiago,Chile,7-13December 2015,2015:1440-1448.
REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards real time object detection with region proposal networks[C]//In Proceedings of the International Conference on Neural Information Processing Systems,Montreal,QC,Canada,7-12 December2015;MIT Press:Cambridge,MA,USA,2015:91-99.
LIU W,ANGUELOV D,ERHAN D,et al.SSD:Single Shot MultiBox Detector[C].European Conference on Computer Vision.2016:21-37.
REDMON J,DIVVALA S,GIRSHICK R,et al.You Only Look Once:Unified,Real-Time Object Detection[C].Proceedings of the IEEE conference on computer vision and pattern recognition.2016:779-788.
REDMON J,FARHADI A.Better,Faster,Stronger[C].2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR)-Honolulu,HI,2017:6517-6525.
REDMON J,FARHADI A.YOLOv3:An Incremental Improvement[J].2018.
刘鹏.基于深度学习的高分辨率遥感影像目标检测方法研究[D].中国科学院大学(中国科学院遥感与数字地球研究所),2018.LIU P.Research on object detection methods of high-resolution remote sensing images based on deep learning[D].University of the Chinese Academy of Sciences(institute of remote sensing and digital earth,Chinese Academy of Sciences),2018.(in Chinese)
XU Y,ZHU M,PENG X,et al.Rapid airplane detection in remote sensing images based on multilayer feature fusion in fully convolutional neural networks[J].Sensors,2018,18(7):2335.
LI H,FU K,YAN M,et al.Vehicle detection in remote sensing images using denoizing-based convolutional neural networks[J].Remote Sensing Letters,2017,8(3):262-270.
TANG T,ZHOU S,DENG Z,et al.Vehicle detection in aerial images based on region convolutional neural networks and hard negative example mining[J].Sensors,2017(17):336.
DENG Z,SUN H,ZHOU S,et al.Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2017:1-13.
CHENG G,ZHOU P,et al.Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J].IEEE Trans Geosci Remote Sens,2016,54(12):7405-7415.
SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for Large-Scale image recognition[C]//The 3rd International Conference on Learning Representation.San Diego,Canada,2015:1-5.
杨东旭,赖惠成,班俊硕,等.基于改进DCNN结合迁移学习的图像分类方法[J].新疆大学学报(自然科学版),2018,35(02):195-202.YANG D,LAN H,BAN J,et al.Image classification method based on improved DCNN combined transfer learning[J].Journal of Xinjiang University(Natural Science Edition),2018,35(02):195-202.(in Chinese)
CHENG G,ZHOU P,et al.Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J].IEEE Transactions on Geoscience and Remote Sensing,2016:1-11.
0
Views
531
下载量
0
CSCD
Publicity Resources
Related Articles
Related Author
Related Institution
京公网安备11010802024621