Yuchen Yao

Object detection in complex environments remains challenging due to illumination variations, background clutter, and the presence of small objects. Multimodal detection methods based on RGB and infrared (IR) data have shown promising potential by leveraging complementary information across modalities. However, existing approaches still suffer from cross-modal feature misalignment, loss of fine-grained details, and insufficient semantic interaction. In this work, we introduce a novel dual-stream framework called DMNet, specifically tailored for visible and IR multimodal object detection. The architecture integrates four core components designed to tackle these challenges: surface detail fusion (SDF) for shallow feature alignment, wavelet feature extraction (WFE) for frequency-domain enhancement, context-guided enhancement (CGE) for semantic refinement, and adaptive spatial fusion (ASF) for multi-scale feature aggregation. We conduct extensive evaluations on three benchmark datasets, including M3FD, LLVIP, and VEDAI, demonstrating that DMNet achieves superior detection performance compared with existing methods. Experimental results confirm that DMNet outperforms existing approaches, achieving an mAP@0.5 of 78.4% on M3FD, 94.4% on LLVIP, and 59.0% on VEDAI. Notably, the model maintains a relatively compact parameter scale (5.72 million parameters) while achieving superior detection performance, making it suitable for practical deployment. These findings highlight DMNet as an effective and efficient solution for multimodal object detection under challenging conditions, especially in low-light and small-object scenarios.