Audio and Multiscale Visual Cues Driven Cross-modal Transformer for Idling Vehicle Detection

Idling vehicle detection (IVD) supports real-time systems that reduce pollution and emissions by dynamically messaging drivers to curb excess idling behavior. In computer vision, IVD has become an emerging task that leverages video from surveillance cameras and audio from remote microphones to localize and classify vehicles in each frame as moving, idling, or engine-off. As with other cross-modal tasks, the key challenge lies in modeling the correspondence between audio and visual modalities, which differ in representation but provide complementary cues -- video offers spatial and motion context, while audio conveys engine activity beyond the visual field. The previous end-to-end model, which uses a basic attention mechanism, struggles to align these modalities effectively, often missing vehicle detections. To address this issue, we propose AVIVDNetv2, a transformer-based end-to-end detection network. It incorporates a cross-modal transformer with global patch-level learning, a multiscale visual feature fusion module, and decoupled detection heads. Extensive experiments show that AVIVDNetv2 improves mAP by 7.66 over the disjoint baseline and 9.42 over the E2E baseline, with consistent AP gains across all vehicle categories. Furthermore, AVIVDNetv2 outperforms the state-of-the-art method for sounding object localization, establishing a new performance benchmark on the AVIVD dataset.