Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network 基于可变形卷积神经网络的遥感影像密集区域车辆检测方法

Xin Gao, Hui Li, Yi Zhang, Menglong Yan, Zongshuo Zhang, Xian Sun, Hao Sun, Hongfeng Yu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.
Original languageEnglish (US)
Pages (from-to)2812-2819
Number of pages8
JournalDianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology
Volume40
Issue number12
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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