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Link to original content: https://unpaywall.org/10.1007/978-981-97-8792-0_36
GSE-Ships: Ship Detection Using Optimized Lightweight Networks and Attention Mechanisms | SpringerLink
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GSE-Ships: Ship Detection Using Optimized Lightweight Networks and Attention Mechanisms

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Pattern Recognition and Computer Vision (PRCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15040))

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Abstract

The detection of ships using Synthetic Aperture Radar (SAR) imagery is crucial in military and civilian sectors, yet balancing speed and accuracy remains challenging. Existing lightweight models aim to reduce parameter count and computational load but often compromise accuracy, leading to increased false alarms. Complex backgrounds, especially near-shore ships against intricate backgrounds, exacerbate SAR ship detection difficulties. This paper introduces an enhanced lightweight ship detection method, GSE-ships, based on the YOLOv5s model, augmented with an attention mechanism. Initially, the proposed GhostMSENet lightweight model facilitates rapid and precise localization and detection of ships within SAR images to fulfill the requirement for real-time detection. Subsequently, integration of the improved attention mechanism enhances the overall detection efficacy of ship objects amidst complex backgrounds post-model training. Lastly, the adoption of the novel ablation function, ECIoU, promotes early convergence and augments ship object detection capabilities. Experimental findings conducted on the SAR ship detection dataset HRSID demonstrate that the enhanced GSE-ships model achieves a reduction in computational burden by 7.97% while enhancing average accuracy by 3.1% compared to YOLOv5s. These results underscore its heightened potential for real-time ship detection applications.

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Author information

Authors and Affiliations

  1. College of Computer and Cyberspace Security, Hebei Normal University, Shijiazhuang, China

    Lina Huo, Huan Li, Wei Wang, Xueyuan Gao, Yifan Wei & Ke Chen

  2. Hebei, China Hebei Provincial Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, 050024, Hebei, China

    Lina Huo, Huan Li, Wei Wang, Xueyuan Gao, Yifan Wei & Ke Chen

  3. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security, Hebei Normal University, Shijiazhuang, 050024, Hebei, China

    Lina Huo, Huan Li, Wei Wang, Xueyuan Gao, Yifan Wei & Ke Chen

Authors
  1. Lina Huo
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  2. Huan Li
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  3. Wei Wang
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  4. Xueyuan Gao
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  5. Yifan Wei
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  6. Ke Chen
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Corresponding author

Correspondence to Wei Wang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Zhouchen Lin

  2. Nankai University, Tianjin, China

    Ming-Ming Cheng

  3. Chinese Academy of Sciences, Beijing, China

    Ran He

  4. Xinjiang University, Ürümqi, Xinjiang, China

    Kurban Ubul

  5. Xinjiang University, Ürümqi, China

    Wushouer Silamu

  6. Peking University, Beijing, China

    Hongbin Zha

  7. Tsinghua University, Beijing, China

    Jie Zhou

  8. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

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Huo, L., Li, H., Wang, W., Gao, X., Wei, Y., Chen, K. (2025). GSE-Ships: Ship Detection Using Optimized Lightweight Networks and Attention Mechanisms. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15040. Springer, Singapore. https://doi.org/10.1007/978-981-97-8792-0_36

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  • DOI: https://doi.org/10.1007/978-981-97-8792-0_36

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  • Publisher Name: Springer, Singapore

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