Object Detection in Remote Sensing Images with Mask R-CNN

Content Provider	Scilit
Author	Gan, Yuhang You, Shucheng Luo, Zhengyu Liu, Ke Zhang, Tao Du, Lei
Copyright Year	2020
Description	Journal: Journal of Physics: Conference Series With the wide applications of remote sensing technology in engineering, the demands of efficient object detection algorithms for remote sensing images have also been significantly increased in recent years. Traditional detection methods have the shortcomings of low accuracy and poor robustness, which is difficult to be applied to remote sensing images with complex background and varieties of objects. Recently, the deep convolutional neural networks have already shown great advances in object detection and outperformed many traditional methods. In this work, we study the performance of a region proposal-based method, Mask R-CNN, for detecting airplane and ship in remote sensing images. Specifically, we add an FPN module to improve the accuracy of small objects detection, and a mask branch is used to describe the shape of objects. In addition, we used a series of data augmentation strategies during training for meeting the CNN’s requirement of training samples diversity. The experimental results show that our model has superior performance in object detection of remote sensing images.
Related Links	https://iopscience.iop.org/article/10.1088/1742-6596/1673/1/012040/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/1673/1/012040
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	1673
Language	English
Publisher	IOP Publishing
Publisher Date	2020-11-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Imaging Science Technology in Engineering Object Detection
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy