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Proceedings Paper

A contextual deep neural network with dilated convolutions for object detection in remote sensing images
Author(s): Shouhong Wan; Xingyue Li; Peiquan Jin; Chang Zou
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Paper Abstract

Object detection is one of the most important issues in the field of remote sensing analysis. The lack of semantic information about objects poses difficulty for traditional methods in exploring effective features for object discrimination. Being capable of feature extraction, a series of region-based convolutional neural networks (R-CNN) have been widely and successfully applied for object detection in natural images recently. However, most of them suffer from the poor detection performance of small-sized targets, which means that few of them can be introduced directly for small-sized object detection in remote sensing images. This paper proposes a modified method based on faster R-CNN, which is composed of a feature extraction network, a region proposal network and an object detection network. Compared to faster R-CNN, in the feature extraction network, the proposed method removes the forth pooling layer and employs dilated convolutions on the all subsequent convolutional layers to enhance the resolution of the final feature maps, which provide more detailed and semantic feature information of targets to help detect objects especially the small-sized one. In the object detection network, contextual features around the region proposals are added as complement feature information to help distinguish objects accurately. Experiments conducted on two data sets verify that our proposal obtains a superior performance on small-sized object detection in remote sensing images.

Paper Details

Date Published: 9 August 2018
PDF: 5 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060W (9 August 2018); doi: 10.1117/12.2502866
Show Author Affiliations
Shouhong Wan, Univ. of Science and Technology of China (China)
Xingyue Li, Univ. of Science and Technology of China (China)
Peiquan Jin, Univ. of Science and Technology of China (China)
Chang Zou, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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