Research on high resolution image object detection technology based on Faster R-CNN

doi:10.6046/gtzyyg.2019.02.06

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Abstract

In order to improve the detection effect of the traditional algorithm on the ground objects in high resolution remote sensing images, this paper applies the deep learning object detection framework Faster R-CNN to the object detection task of high resolution remote sensing images. The airport and aircraft are used as the test scene and detection object for the experiment respectively, The Faster R-CNN framework is trained using the high-resolution remote sensing image data set to obtain the corresponding object detection model. The model is used to detect aircraft objects in high resolution remote sensing images and perform statistical analysis of the experimental results. The experimental results show that the Faster R-CNN model can entirely and accurately detect aircraft objects with an optimal F1 score of 0.976 3, and the same model can be used for object detection of multiple high resolution remote sensing images.

Keywords object detection Faster R-CNN convolution neural network high resolution remote sensing image

TP79

Corresponding Authors: Guoqing YAO E-mail: gqyao@cugb.edu.cn

Issue Date: 23 May 2019

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	Qifang XIE
	Guoqing YAO
	Meng ZHANG

Cite this article:

Qifang XIE,Guoqing YAO,Meng ZHANG. Research on high resolution image object detection technology based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2019, 31(2): 38-43.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.02.06 OR https://www.gtzyyg.com/EN/Y2019/V31/I2/38

Fig.1 Faster R-CNN framework diagram

Fig.2 RPN network structure

Tab.1 Experimental data information

Fig.3 Flow chart of high resolution remote sensing image object detection experiment

Fig.4 Aircraft identification experiment training set

Fig.5 Aircraft identification experiment test results

Fig.6 Aircraft detection experiment training set

Fig.7 Test results of panchromatic remote sensing images for aircrafts

Tab.2 Evaluation indexes of Faster R-CNN model based on panchromatic remote sensing image set

Fig.8 Test results of multispectral color remote sensing images for aircrafts

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