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Remote Sens. 2018, 10(9), 1470; https://doi.org/10.3390/rs10091470

Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images

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: 24 August 2018 / Revised: 9 September 2018 / Accepted: 12 September 2018 / Published: 14 September 2018
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Abstract

The region-based convolutional networks have shown their remarkable ability for object detection in optical remote sensing images. However, the standard CNNs are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. To address this, we introduce a new module named deformable convolution that is integrated into the prevailing Faster R-CNN. By adding 2D offsets to the regular sampling grid in the standard convolution, it learns the augmenting spatial sampling locations in the modules from target tasks without additional supervision. In our work, a deformable Faster R-CNN is constructed by substituting the standard convolution layer with a deformable convolution layer in the last network stage. Besides, top-down and skip connections are adopted to produce a single high-level feature map of a fine resolution, on which the predictions are to be made. To make the model robust to occlusion, a simple yet effective data augmentation technique is proposed for training the convolutional neural network. Experimental results show that our deformable Faster R-CNN improves the mean average precision by a large margin on the SORSI and HRRS dataset. View Full-Text
Keywords: Deformable CNN; Faster R-CNN; data augmentation; occluded object detection Deformable CNN; Faster R-CNN; data augmentation; occluded object detection
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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. Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images. Remote Sens. 2018, 10, 1470.

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