Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution ima
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S.I. : DEEP LEARNING APPROACHES FOR REALTIME IMAGE SUPER RESOLUTION (DLRSR)
Arbitrary-oriented object detection via dense feature fusion and attention model for remote sensing super-resolution image Fuhao Zou1 • Wei Xiao1 • Wanting Ji2 • Kunkun He1 • Zhixiang Yang3 • Jingkuan Song4 • Helen Zhou5 Kai Li1
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Received: 12 April 2019 / Accepted: 25 March 2020 Ó Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract In this paper, we aim at developing a new arbitrary-oriented end-to-end object detection method to further push the frontier of object detection for remote sensing image. The proposed method comprehensively takes into account multiple strategies, such as attention mechanism, feature fusion, rotation region proposal as well as super-resolution pre-processing simultaneously to boost the performance in terms of localization and classification under the faster RCNN-like framework. Specifically, a channel attention network is integrated for selectively enhancing useful features and suppressing useless ones. Next, a dense feature fusion network is designed based on multi-scale detection framework, which fuses multiple layers of features to improve the sensitivity to small objects. In addition, considering the objects for detection are often densely arranged and appear in various orientations, we design a rotation anchor strategy to reduce the redundant detection regions. Extensive experiments on two remote sensing public datasets DOTA, NWPU VHR-10 and scene text dataset ICDAR2015 demonstrate that the proposed method can be competitive with or even superior to the state-of-the-art ones, like R2CNN and R2CNN??. Keywords Object detection Arbitrary oriented Rotation proposals Remote sensing image Attention model Dense feature pyramid network Super-resolution
1 Introduction Automatically object detection for remote sensing image is usually a significant prerequisite for the visual recognition tasks, such as object coarse or fine-grained classification, & Fuhao Zou [email protected] 1
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2
School of Natural and Computational Science, Massey University, Auckland, New Zealand
3
Wuhan Digital Engineering Research Institute, Wuhan, China
4
Innovation Center, University of Electronic Science and Technology of China, Chengdu, China
5
School of Engineering, Manukau Institute of Technology, Auckland, New Zealand
object attribute learning, object counting and analysis of battle-field situation. Thus, object detection in remote sensing image has attracted a large amount of attentions in past decades. This phenomenon is further pushed to a new height by the success of deep convolutional network (DCNN) [1] in various computer vision tasks. Strongly promoted by the advance in DCNN, a large body of object detection methods have been springed up, which mainly contain horizontal and rotation region-based methods. The representative horizontal r
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