Semantic attention-based network for inshore SAR ship detection

Wenhao Sun; Xiayuan Huang

doi:10.1117/12.2600839

30 June 2021 Semantic attention-based network for inshore SAR ship detection

Wenhao Sun, Xiayuan Huang

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118782A (2021) https://doi.org/10.1117/12.2600839
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

The performance of Synthetic Aperture Radar (SAR) ship detector has been significantly improved with the development of convolutional neural network. However, the issue of effective detection of inshore ships is still a challenging problem. In this paper, we propose a novel one-stage SAR ship detector, called Semantic Attention-Based Network (SANet), which can largely improve the accuracy of ship detection in the inshore scenario without compromising the speed. Specifically, we introduce a semantic attention mechanism, which will highlight the features from the ships area and enhance the detector's classification ability. We train the proposed Semantic Attention Module with focal loss, and assign labels for the attention maps by center sampling. Combined with our anchor assign strategy, our SANet achieves state-of-the-art results on the open SAR Ship Detection Dataset (SSDD).

Citation Download Citation

Wenhao Sun and Xiayuan Huang "Semantic attention-based network for inshore SAR ship detection", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118782A (30 June 2021); https://doi.org/10.1117/12.2600839

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