Moving vehicle detection using acoustic and seismic signals with cross-modal fusion model

Abstract This paper investigates field moving vehicle detection using field-measured acoustic and seismic signals and proposes a detection algorithm based on cross-modal fusion model. First, the proposed method specifically extracts complementary features from both modalities and designs a transformer-based fusion framework. Second, three key components are further introduced: the modality alignment modules are employed to balance and improve feature representation quality; the bidirectional cross-attention layer is adopted to enable mutual learning and information complementation across modalities; and the gated fusion module is incorporated to ensure effective integration and discriminability of fused features. Finally, we construct a 76 h second-level vehicle detection dataset to evaluate the proposed fusion method. Ablation experiments verify the effectiveness of the important modules and the overall performance of the proposed method. Comparative results against existing detection algorithms and fusion-based methods demonstrate that our approach achieves superior performance, with F1-score of 0.8722, error rate of 0.0720, and AUC of 0.9652. While significantly improving vehicle detection performance, the proposed method maintains a moderate level of computational complexity, making it a promising solution for embedded applications.