The application of deep learning-based methods in ship detection remains challenging due to the small size of the target of interest and the interference of complex sea surface background. Meanwhile, existing ship detection methods tend to experiment on a single image type, but rarely verify the applicability of their methods to multi-source sensor images. Therefore, this paper proposes a multi-scale deformable ship detector (MDSD) method based on the YOLOv3 framework for ship detection in optical and synthetic aperture radar (SAR) images. Since various information in the complex sea surface background can interfere with target detection, the detection algorithm designed in this paper includes a new multi-scale deformable attention module (MDAM), which can be added to the FPN part of the YOLOv3 model to enable the network to highlight target features while suppressing background features. The proposed MDAM generates attention maps from feature maps with multiscale deformable sensory fields, which can be better adapted to ship objects of various shapes and sizes. Compared with other models, MDSD has the highest detection accuracy on both optical image dataset (LEVIR) and SAR image dataset (SSDD). Also, MDSD is robust, which is validated on mixed datasets containing optical and SAR images.
Optical and SAR remote sensing image ship detection based on attention mechanism
Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023) ; 2023 ; Qingdao, SD, China
Proc. SPIE ; 12791
2023-10-09
Conference paper
Electronic Resource
English
Attention-Based Convolutional Networks for Ship Detection in High-Resolution Remote Sensing Images
British Library Conference Proceedings | 2018
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