|Table of Contents|

Target Detection of Rural Buildings in UAV Remote Sensing ImagesBased on Convolutional Neural Network(PDF)

南京师范大学学报（工程技术版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2019年03期
Page:
29-
Research Field:
计算机工程
Publishing date:

Info

Title:
Target Detection of Rural Buildings in UAV Remote Sensing ImagesBased on Convolutional Neural Network
Author(s):
Ren Yuanyuan1; 2; Zhang Xianfeng2; 3; Ma Yongjian1; 2; Yang Qiyuan1; 2; Wang Chuanjian1; 2; Dai Jianguo1; 2; Zhao Qingzhan1; 2
Ren Yuanyuan1,2,Zhang Xianfeng2,3,Ma Yongjian1,2,Yang Qiyuan1,2,Wang Chuanjian1,2,Dai Jianguo1,2,Zhao Qingzhan1,2
Keywords:
buildings; detection; UAV; deep learning; convolutional neural network; Faster R-CNN
PACS:
TP391
DOI:
10.3969/j.issn.1672-1292.2019.03.005
Abstract:
With deep learning applied to object recognition of remote sensing images,a method of object detection for rural buildings based on convolution neural network is proposed in the paper. The improved Faster R-CNN network model is trained in an end-to-end way and applied to rural buildings with the rapid and accurate identification. Specifically,the method mainly includes region recommendation based on RPN network and convolutional neural network model training based on Inception v2. In order to train and test the improved model,the remote sensing images of rural buildings in southern Xinjiang Region are collected by UAV,and the data set of rural buildings is established by manual labeling. Finally,the model is validated by the object detection of the data set with TensorFlow deep learning framework. Experimental results show that the overall accuracy of fast and accurate recognition of UAV remote sensing images based on the improved convolution neural network object detection method exceeds 90%. By updating the initial parameters,the model converges faster,which has a certain reference value for the classification and object recognition of UAV remote sensing images.

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Last Update: 2019-09-30