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Peer-reviewedCC BY 4.0PLOS One

LHR-YOLO: A SAR small-target ship detection method based on improved YOLO11

  • Miao Zhou1
  • Dezhi Han2
  • Xiang Shen
  • Yangshuyi Xu
  • Chongqing Chen
  1. 1School of Information Engineering, Shanghai Maritime University, Shanghai, China
  2. 2School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia
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Abstract

Synthetic Aperture Radar (SAR) ship target detection holds significant application value in maritime traffic monitoring and marine environmental monitoring. However, due to challenges such as small ship targets, complex marine backgrounds, and speckle noise interference, existing methods still suffer from insufficient accuracy in small-target detection scenarios. To address this issue, this paper proposes a high-resolution detection algorithm named LHR-YOLO, based on YOLO11. In the Stem stage, a Gaussian–Laplacian edge-enhancement and Gaussian-filtering mechanism is introduced to effectively improve shallow-feature perception for tiny ship targets. In the Backbone, an improved C3k2 module is designed by incorporating channel shuffling, re-parameterized convolutions, and feature aggregation strategies to enhance multi-scale feature representation. Meanwhile, in the Neck–Detect stage, high-frequency perception, spatial dependency modeling, and a high-resolution detection head are integrated to further improve the localization accuracy of extremely small targets. Experimental results show that LHR-YOLO achieves mAP50:95 values of 71.59% and 73.40% on the HRSID and SSDD datasets, respectively. Compared with YOLO11n, LHR-YOLO improves mAPS on HRSID from 53.95% to 60.53%, corresponding to an increase of 6.58 percentage points. These results validate the effectiveness of the proposed algorithm for detecting tiny ships in complex SAR maritime scenes and provide technical support for high-precision maritime target monitoring.The source code is available at: https://github.com/LRYTH/LHR-YOLO.

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Cite

BibTeXRIS
BibTeX
@article{zhou2026lhryolo,
  title = {{LHR-YOLO: A SAR small-target ship detection method based on improved YOLO11}},
  author = {Miao Zhou and Dezhi Han and Xiang Shen and Yangshuyi Xu and Chongqing Chen},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0359334},
  url = {https://doi.org/10.1371/journal.pone.0359334},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - LHR-YOLO: A SAR small-target ship detection method based on improved YOLO11
AU  - Miao Zhou
AU  - Dezhi Han
AU  - Xiang Shen
AU  - Yangshuyi Xu
AU  - Chongqing Chen
PY  - 2026
DA  - 2026/09/24
JO  - PLOS One
DO  - 10.1371/journal.pone.0359334
UR  - https://doi.org/10.1371/journal.pone.0359334
AB  - Synthetic Aperture Radar (SAR) ship target detection holds significant application value in maritime traffic monitoring and marine environmental monitoring. However, due to challenges such as small ship targets, complex marine backgrounds, and speckle noise interference, existing methods still suffer from insufficient accuracy in small-target detection scenarios. To address this issue, this paper proposes a high-resolution detection algorithm named LHR-YOLO, based on YOLO11. In the Stem stage, a Gaussian–Laplacian edge-enhancement and Gaussian-filtering mechanism is introduced to effectively improve shallow-feature perception for tiny ship targets. In the Backbone, an improved C3k2 module is designed by incorporating channel shuffling, re-parameterized convolutions, and feature aggregation strategies to enhance multi-scale feature representation. Meanwhile, in the Neck–Detect stage, high-frequency perception, spatial dependency modeling, and a high-resolution detection head are integrated to further improve the localization accuracy of extremely small targets. Experimental results show that LHR-YOLO achieves mAP50:95 values of 71.59% and 73.40% on the HRSID and SSDD datasets, respectively. Compared with YOLO11n, LHR-YOLO improves mAPS on HRSID from 53.95% to 60.53%, corresponding to an increase of 6.58 percentage points. These results validate the effectiveness of the proposed algorithm for detecting tiny ships in complex SAR maritime scenes and provide technical support for high-precision maritime target monitoring.The source code is available at: https://github.com/LRYTH/LHR-YOLO.
SN  - 1932-6203
ER  -