Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network

Xin GAO; Hui LI; Yi ZHANG; Menglong YAN; Zongshuo ZHANG; Xian SUN; Hao SUN; Hongfeng YU

doi:10.11999/JEIT180209

Volume 40 Issue 12

Nov. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(12): 2812-2819

Xin GAO, Hui LI, Yi ZHANG, Menglong YAN, Zongshuo ZHANG, Xian SUN, Hao SUN, Hongfeng YU. Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209

Citation:

Xin GAO, Hui LI, Yi ZHANG, Menglong YAN, Zongshuo ZHANG, Xian SUN, Hao SUN, Hongfeng YU. Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209

Citation:

Xin GAO, Hui LI, Yi ZHANG, Menglong YAN, Zongshuo ZHANG, Xian SUN, Hao SUN, Hongfeng YU. Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209

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Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network

doi: 10.11999/JEIT180209

1.
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
University of Central Lancashire (UCLan), Preston PR1 2HE, United Kingdom

Funds: The National Natural Science Foundation of China (41501485)

Received Date: 2018-03-02
Rev Recd Date: 2018-06-11

Available Online: 2018-07-16

Publish Date: 2018-12-01

Abstract

Abstract

Vehicle detection is one of the hotspots in the field of remote sensing image analysis. The intelligent extraction and identification of vehicles are of great significance to traffic management and urban construction. In remote sensing field, the existing methods of vehicle detection based on Convolution Neural Network (CNN) are complicated and most of these methods have poor performance for dense areas. To solve above problems, an end-to-end neural network model named DF-RCNN is presented to solve the detecting difficulty in dense areas. Firstly, the model unifies the resolution of the deep and shallow feature maps and combines them. After that, the deformable convolution and RoI pooling are used to study the geometrical deformation of the target by adding a small number of parameters and calculations. Experimental results show that the proposed model has good detection performance for vehicle targets in dense areas.
- Remote sensing images,
- Vehicle detection,
- Dense areas,
- End-to-end convolution neural network

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References(21)

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