Self-supervised SAR ship detection

Xiaokang Ren; Nannan Cai

doi:10.1117/12.2660019

3 February 2023 Self-supervised SAR ship detection

Xiaokang Ren, Nannan Cai

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125111K (2023) https://doi.org/10.1117/12.2660019
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

Synthetic Aperture Radar (SAR) has become one of the primary means of current earth observation due to its unique technical advantages, such as all-weather, all-time, and extended operating distance. However, most SAR detectors based on deep learning methods use outdated ResNet backbone networks, and the detection model detection accuracy is low. This paper proposes a new network called Dynamic IoU R-CNN (DIoU R-CNN) to transfer the self-supervised learning method moby based on Swin Transformer to the complex downstream task of SAR ship detection. DIoU R-CNN adds the dynamic IoU module to Faster R-CNN and the advanced BalancedL1 loss function, achieves relatively high accuracy SAR ship detection in the SSDD dataset without much increase in the number of parameters and training time. And the Swin Transformer with self-supervised learning performs even better than the supervised learning method in the comparison experiments.

Citation Download Citation

Xiaokang Ren and Nannan Cai "Self-supervised SAR ship detection", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125111K (3 February 2023); https://doi.org/10.1117/12.2660019

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KEYWORDS

Synthetic aperture radar

Transformers

Sensors

Data modeling

Machine learning

Computer vision technology

Detection and tracking algorithms

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