Abstract
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.
B. Fan and W. Chen—The first two authors contribute equally to this work.
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Notes
- 1.
Datasets Annotation Tool. https://github.com/tzutalin/labelImg.
- 2.
Underwater Robot Picking Contest. http://www.cnurpc.org/.
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Acknowledgments
This work is supported by the Ministry of Science and Technology of the People’s Republic of China (2019YFB1310300), National Natural Science Foundation of China (No. 61876092), State Key Laboratory of Robotics (No. 2019-O07) and State Key Laboratory of Integrated Service Network (ISN20-08).
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Fan, B., Chen, W., Cong, Y., Tian, J. (2020). Dual Refinement Underwater Object Detection Network. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12365. Springer, Cham. https://doi.org/10.1007/978-3-030-58565-5_17
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