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Dual Refinement Underwater Object Detection Network
Fan BJ(范保杰)1; Chen, Wei1; Cong Y(丛杨)2; Tian JD(田建东)2
Conference Name16th European Conference on Computer Vision, ECCV 2020
Conference DateAugust 23-28, 2020
Conference PlaceGlasgow, United kingdom
Source PublicationComputer Vision – ECCV 2020 - 16th European Conference 2020, Proceedings
PublisherSpringer Science and Business Media Deutschland GmbH
Publication PlaceBerlin
Indexed ByEI
EI Accession number20205009616430
Contribution Rank2
KeywordUnderwater object detection Feature enhancement Anchor refinement Underwater dataset
AbstractDue 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: © 2020, Springer Nature Switzerland AG.
Document Type会议论文
Corresponding AuthorFan BJ(范保杰)
Affiliation1.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
Recommended Citation
GB/T 7714
Fan BJ,Chen, Wei,Cong Y,et al. Dual Refinement Underwater Object Detection Network[C]. Berlin:Springer Science and Business Media Deutschland GmbH,2020:275-291.
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