A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Author to whom correspondence should be addressed.
Received: 22 May 2019 / Revised: 1 July 2019 / Accepted: 2 July 2019 / Published: 4 July 2019
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Object detection is a significant and challenging problem in the study area of remote sensing and image analysis. However, most existing methods are easy to miss or incorrectly locate objects due to the various sizes and aspect ratios of objects. In this paper, we propose a novel end-to-end Adaptively Aspect Ratio Multi-Scale Network (A
RMNet) to solve this problem. On the one hand, we design a multi-scale feature gate fusion network to adaptively integrate the multi-scale features of objects. This network is composed of gate fusion modules, refine blocks and region proposal networks. On the other hand, an aspect ratio attention network is leveraged to preserve the aspect ratios of objects, which alleviates the excessive shape distortions of objects caused by aspect ratio changes during training. Experiments show that the proposed A
RMNet significantly outperforms the previous state of the arts on the DOTA dataset, NWPU VHR-10 dataset, RSOD dataset and UCAS-AOD dataset by
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Qiu, H.; Li, H.; Wu, Q.; Meng, F.; Ngan, K.N.; Shi, H. A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images. Remote Sens. 2019, 11, 1594.
Qiu H, Li H, Wu Q, Meng F, Ngan KN, Shi H. A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images. Remote Sensing. 2019; 11(13):1594.
Qiu, Heqian; Li, Hongliang; Wu, Qingbo; Meng, Fanman; Ngan, King N.; Shi, Hengcan. 2019. "A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images." Remote Sens. 11, no. 13: 1594.
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