Maritime surveillance plays a vital role in reducing maritime accidents and improving maritime safety. To enhance situational awareness for maritime movements, deep learning-based visual object detection has become an important part of maritime surveillance. However, the detection results are highly dependent on the training datasets collected from different departments (i.e., clients). If the sub-datasets from departments are sensitive and private in cross-department maritime surveillance, it will be intractable to directly combine these sub-datasets to train the learning-based object detection method. To solve this issue, we propose a federated learning-driven cross-spatial vessel detection model, called FLCSDet, for maritime surveillance with privacy preservation. In particular, an efficient multi-scale attention module is integrated into our FLCSDet to achieve local cross-spatial feature learning. To improve the federated-learning aggregation method, we propose an optimized algorithm based on the proportion of valid data on departments to adaptively select the allocating weights and preserve the specific characteristics of client data. In addition, we employ transfer learning to further improve the robustness and convergence of our FLCSDet under different experimental scenarios. Compared with several representative federated learning-based detection methods, our FLCSDet could achieve superior detection performance in terms of both quantitative and qualitative results. Moreover, comprehensive experiments conducted on real datasets from both inland waterways and open seas demonstrate the robustness and generalization of our method in intelligent transportation systems. The source code is available at https://github.com/huangyanh/FLCSDet.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    FLCSDet: Federated Learning-Driven Cross-Spatial Vessel Detection for Maritime Surveillance With Privacy Preservation


    Contributors:
    Huang, Yanhong (author) / Liu, Wen (author) / Lin, Yijing (author) / Kang, Jiawen (author) / Zhu, Fenghua (author) / Wang, Fei-Yue (author)


    Publication date :

    2025-01-01


    Size :

    5119783 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Multi-Task Learning-Enabled Automatic Vessel Draft Reading for Intelligent Maritime Surveillance

    Qu, Jingxiang / Liu, Ryan Wen / Zhao, Chenjie et al. | IEEE | 2024


    Online learning for ship detection in maritime surveillance

    Wijnhoven, Rob / Rens, Kris van / Jaspers, Egbert G.T. et al. | Tema Archive | 2010


    Federated Transfer Learning for Privacy-Preserved Cross-City Traffic Flow Prediction

    Yuan, Xiaoming / Luo, Zhenyu / Zhang, Ning et al. | IEEE | 2025


    Maritime surveillance system

    KANG YOUNG SHIN / KOO SAM OK / PARK BUM JIN et al. | European Patent Office | 2017

    Free access