SwinInsSeg: An Improved SOLOv2 Model Using the Swin Transformer and a Multi-Kernel Attention Module for Ship Instance Segmentation

Rabi Sharma (), Muhammad Saqib, Chin-Teng Lin and Michael Blumenstein
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Rabi Sharma: School of Computer Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia
Muhammad Saqib: School of Computer Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia
Chin-Teng Lin: School of Computer Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia
Michael Blumenstein: School of Computer Science, University of Technology Sydney, Broadway, Ultimo, NSW 2007, Australia

Mathematics, 2025, vol. 13, issue 1, 1-22

Abstract: Maritime surveillance is essential for ensuring security in the complex marine environment. The study presents SwinInsSeg, an instance segmentation model that combines the Swin transformer and a lightweight MKA module to segment ships accurately and efficiently in maritime surveillance. Current models have limitations in segmenting multiscale ships and achieving accurate segmentation boundaries. SwinInsSeg addresses these limitations by identifying ships of various sizes and capturing finer details, including both small and large ships, through the MKA module, which emphasizes important information at different processing stages. Performance evaluations on the MariBoats and ShipInsSeg datasets show that SwinInsSeg outperforms YOLACT, SOLO, and SOLOv2, achieving mask average precision scores of 50.6% and 52.0%, respectively. These results demonstrate SwinInsSeg’s superior capability in segmenting ship instances with improved accuracy.

Keywords: instance segmentation; maritime surveillance; convolutional neural network; transformers; attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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