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FESAR: SAR ship detection model based on local spatial relationship capture and fused convolutional enhancement

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Abstract

Synthetic aperture radar (SAR) is instrumental in ship monitoring owing to its all-weather capabilities and high resolution. In SAR images, ship targets frequently display blurred or mixed boundaries with the background, and instances of occlusion or partial occlusion may occur. Additionally, multi-scale transformations and small-target ships pose challenges for ship detection. To tackle these challenges, we propose a novel SAR ship detection model, FESAR. Firstly, in addressing multi-scale transformations in ship detection, we propose the Fused Convolution Enhancement Module (FCEM). This network incorporates distinct convolutional branches designed to capture local and global features, which are subsequently fused and enhanced. Secondly, a Spatial Relationship Analysis Module (SRAM) with a spatial-mixing layer is designed to analyze the local spatial relationship between the ship target and the background, effectively combining local information to discern feature distinctions between the ship target and the background. Finally, a new backbone network, SPD-YOLO, is designed to perform deep downsampling for the comprehensive extraction of semantic information related to ships. To validate the model’s performance, an extensive series of experiments was conducted on the public datasets HRSID, LS-SSDD-v1.0, and SSDD. The results demonstrate the outstanding performance of the proposed FESAR model compared to numerous state-of-the-art (SOTA) models. Relative to the baseline model, FESAR exhibits an improvement in mAP by 2.6% on the HRSID dataset, 5.5% on LS-SSDD-v1.0, and 0.2% on the SSDD dataset. In comparison with numerous SAR ship detection models, FESAR demonstrates superior comprehensive performance.

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Availability of data and materials

The source code of the project used in this experiment is open source. From the open source website https://github.com. The datasets SSDD, LS-SSDD-v1.0 and HRSID used in the paper are both publicly available online datasets, and the datasets are also cited in the references.

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Funding

This study was not supported by any funding agency or project grant.

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Authors and Affiliations

  1. College of Physics and Electronic Engineering, Northwest Normal University, Peili Street, Lanzhou, 730070, Gansu, China

    Chongchong Liu & Chunman Yan

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  1. Chongchong Liu
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  2. Chunman Yan
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Contributions

Chongchong Liu contributed to conceptualization, methodology, software, writing, data curation, and visualization. Chunman Yan contributed to methodology, project administration, and supervision

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Correspondence to Chunman Yan.

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We declare that there is no interest of a financial or personal nature associated with this study. The design, conduct, data analysis, and interpretation of the results of this study are free from any potential interest.

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Liu, C., Yan, C. FESAR: SAR ship detection model based on local spatial relationship capture and fused convolutional enhancement. Machine Vision and Applications 35, 34 (2024). https://doi.org/10.1007/s00138-024-01516-4

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