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Appl. Sci. 2018, 8(5), 813; https://doi.org/10.3390/app8050813

Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN

1
ATR National Lab, National University of Defense Technology, Changsha 410073, China
2
State Key Lab of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
Received: 20 April 2018 / Revised: 11 May 2018 / Accepted: 16 May 2018 / Published: 18 May 2018
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Abstract

The PASCAL VOC Challenge performance has been significantly boosted by the prevalently CNN-based pipelines like Faster R-CNN. However, directly applying the Faster R-CNN to the small remote sensing objects usually renders poor performance. To address this issue, this paper investigates on how to modify Faster R-CNN for the task of small object detection in optical remote sensing images. First of all, we not only modify the RPN stage of Faster R-CNN by setting appropriate anchors but also leverage a single high-level feature map of a fine resolution by designing a similar architecture adopting top-down and skip connections. In addition, we incorporate context information to further boost small remote sensing object detection performance while we apply a simple sampling strategy to solve the issue about the imbalanced numbers of images between different classes. At last, we introduce a simple yet effective data augmentation method named ‘random rotation’ during training. Experimental results show that our modified Faster R-CNN algorithm improves the mean average precision by a large margin on detecting small remote sensing objects. View Full-Text
Keywords: object detection; modified faster R-CNN; remote sensing; feature pyramid object detection; modified faster R-CNN; remote sensing; feature pyramid
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Ren, Y.; Zhu, C.; Xiao, S. Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN. Appl. Sci. 2018, 8, 813.

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