Dual Refinement Underwater Object Detection Network

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	Dual Refinement Underwater Object Detection Network
	Fan BJ(范保杰)1; Chen, Wei 1; Cong Y(丛杨)2; Tian JD(田建东)2
	2020
会议日期	August 23-28, 2020
会议地点	Glasgow, United kingdom
关键词	Underwater object detection Feature enhancement Anchor refinement Underwater dataset
页码	275-291
英文摘要	Due to the complex underwater environment, underwater imaging often encounters some problems such as blur, scale variation, color shift, and texture distortion. Generic detection algorithms can not work well when we use them directly in the underwater scene. To address these problems, we propose an underwater detection framework with feature enhancement and anchor refinement. It has a composite connection backbone to boost the feature representation and introduces a receptive field augmentation module to exploit multi-scale contextual features. The developed underwater object detection framework also provides a prediction refinement scheme according to six prediction layers, it can refine multi-scale features to better align with anchors by learning from offsets, which solve the problem of sample imbalance to a certain extent. We also construct a new underwater detection dataset, denoted as UWD, which has more than 10,000 train-val and test underwater images. The extensive experiments on PASCAL VOC and UWD demonstrate the favorable performance of the proposed underwater detection framework against the states-of-the-arts methods in terms of accuracy and robustness. Source code and models are available at: https://github.com/Peterchen111/FERNet. © 2020, Springer Nature Switzerland AG.
产权排序	2
会议录	Computer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
会议录出版者	Springer Science and Business Media Deutschland GmbH
会议录出版地	Berlin
语种	英语
ISSN号	0302-9743
ISBN号	978-3-030-58564-8
内容类型	会议论文
源URL	[http://ir.sia.cn/handle/173321/28358]
专题	沈阳自动化研究所_机器人学研究室
通讯作者	Fan BJ(范保杰)
作者单位	1.College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China 2.Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, Shenyang 110016, China
推荐引用方式 GB/T 7714	Fan BJ,Chen, Wei,Cong Y,et al. Dual Refinement Underwater Object Detection Network[C]. 见:. Glasgow, United kingdom. August 23-28, 2020.