Frequency–Time Integrated Transformer for Event Recognition in Complex Distributed Acoustic Sensing Environments

The widespread application of Distributed Acoustic Sensing (DAS) in security monitoring has increased the demand for advanced intrusion recognition algorithms. Conventional approaches often divide the process into two stages: signal processing and recognition modeling. However, this separation introduces two key challenges: (1) signal processing quality is typically assessed only through the final recognition outcome, lacking a unified optimization mechanism, which limits efficiency and generalization. (2) The two-stage framework is unsuitable for tasks requiring high real-time performance. Therefore, we propose the Frequency-Time Integrated Transformer (FTIformer) to address these challenges. FTIformer integrates the Multi-head Time-Frequency Perception (MTFP) module and the Self-Attention Module (SAM), achieving unified optimization of time-frequency signal extraction and pattern recognition through loss function backpropagation. Furthermore, in complex environments, multiple events of interest may occur within the same detection cycle, causing feature overlap and potential confusion of model outputs. To mitigate this, we decompose the multi-event recognition task into multiple single-event recognition tasks. Building on this approach, class-specific loss is proposed for the first time, which guides the model in learning unique representations for each event. We conducted experiments using a private dataset and a public dataset. Experimental results indicate that FTIformer achieves an accuracy, precision, and recall of 0.946, 0.976, and 0.979, respectively, on the private dataset, and 0.897, 0.956, and 0.966 on the public dataset. The detailed implementation of the proposed model is available at https://github.com/linjie1888/FTIformer-notebook.