ADFN: Adaptive Dynamic Fusion Network for Real-time Multispectral Object Detection

Abstract

Multispectral object detection leverages the complementary strengths of infrared (IR) and visible (VIS) modalities to improve detection accuracy. However, existing approaches often lack adaptability to dynamic lighting conditions, or fail to achieve real-time performance due to complexity. We propose the Adaptive Dynamic Fusion Network (ADFN), a novel architecture that integrates adaptive multi-path computation and attention-guided feature fusion to address these challenges. ADFN incorporates the Collaborative and Alternating Attention (CAA) modules for efficient feature alignment and the Adaptive Dynamic Pathway (ADP) strategy to dynamically adjust computational pathways based on lighting conditions, optimizing the balance between accuracy and efficiency. Experiments on the FLIR2 and LLVIP datasets demonstrate that ADFN achieves superior mAP@50-95 and real-time performance, showcasing its robustness and efficiency across diverse environments. ADFN offers a practical solution for dynamic lighting conditions and resource-constrained multispectral object detection tasks.