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

Abstract

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 <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/huangyanh/FLCSDet</uri>.

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