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Remote Sens. 2017, 9(12), 1312; https://doi.org/10.3390/rs9121312

Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery

1
School of Electronic Information, Wuhan University, Wuhan 430072, Hubei, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, Hubei, China
*
Author to whom correspondence should be addressed.
Received: 11 October 2017 / Revised: 12 December 2017 / Accepted: 13 December 2017 / Published: 13 December 2017
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Abstract

Convolutional neural networks (CNNs) have demonstrated their ability object detection of very high resolution remote sensing images. However, CNNs have obvious limitations for modeling geometric variations in remote sensing targets. In this paper, we introduced a CNN structure, namely deformable ConvNet, to address geometric modeling in object recognition. By adding offsets to the convolution layers, feature mapping of CNN can be applied to unfixed locations, enhancing CNNs’ visual appearance understanding. In our work, a deformable region-based fully convolutional networks (R-FCN) was constructed by substituting the regular convolution layer with a deformable convolution layer. To efficiently use this deformable convolutional neural network (ConvNet), a training mechanism is developed in our work. We first set the pre-trained R-FCN natural image model as the default network parameters in deformable R-FCN. Then, this deformable ConvNet was fine-tuned on very high resolution (VHR) remote sensing images. To remedy the increase in lines like false region proposals, we developed aspect ratio constrained non maximum suppression (arcNMS). The precision of deformable ConvNet for detecting objects was then improved. An end-to-end approach was then developed by combining deformable R-FCN, a smart fine-tuning strategy and aspect ratio constrained NMS. The developed method was better than a state-of-the-art benchmark in object detection without data augmentation. View Full-Text
Keywords: deformable ConvNet; very high resolution remote sensing imagery; object detection; training mechanism; non maximum suppression deformable ConvNet; very high resolution remote sensing imagery; object detection; training mechanism; non maximum suppression
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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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Xu, Z.; Xu, X.; Wang, L.; Yang, R.; Pu, F. Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery. Remote Sens. 2017, 9, 1312.

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