Abstract
Ocean front is an ocean phenomenon, which has important impact on marine ecosystems and marine fisheries. Hence, it is of great significance to study ocean front detection. So far, some ocean front detection methods have been proposed. However, there are mainly two problems for these existing methods: one is the lack of labeled ocean front detection data sets, and the other is that there is no deep learning methods used to locate accurate position of ocean fronts. In this paper, we design a bi-directional edge detection network (BEDNet) based on our collected ocean front data set to tackle these two problems. The labeled ocean front data set is named OFDS365, which consists of 365 images based on the gradient of sea surface temperature (SST) images acquired at every day of the year 2014. BEDNet mainly contains four stages, a pathway from shallow stages to deep stages, and a pathway from deep stages to shallow stages, which can achieve bi-directional multi-scale information fusion. Moreover, we combine the dice and cross-entropy loss function to train our network, obtaining the fine-grained ocean front detection results. In the experiments, we show that BEDNet achieves better performance on ocean front detection compared with other existing methods.
Q. Li and Z. Fan—have equal contributions.
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Acknowledgments
This work was supported by the Major Project for New Generation of AI under Grant No. 2018AAA0100400, the National Natural Science Foundation of China (NSFC) under Grant No. 41706010, the Joint Fund of the Equipments Pre-Research and Ministry of Education of China under Grand No. 6141A020337, and the Fundamental Research Funds for the Central Universities of China.
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Li, Q., Fan, Z., Zhong, G. (2020). BEDNet: Bi-directional Edge Detection Network for Ocean Front Detection. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_35
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DOI: https://doi.org/10.1007/978-3-030-63820-7_35
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