FQDNet: A Fusion-Enhanced Quad-Head Network for RGB-Infrared Object Detection

RGB-IR object detection provides a promising solution for complex scenarios, such as remote sensing and low-light environments, by leveraging the complementary strengths of visible and infrared modalities. Despite significant advancements, two key challenges remain: (1) effectively integrating multi-modal features within lightweight frameworks to enable real-time performance and (2) fully utilizing multi-scale features, which are crucial for detecting objects of varying sizes but are often underexploited, leading to suboptimal accuracy. To address these challenges, we propose FQDNet, a novel RGB-IR object detection network that integrates an optimized fusion strategy with a Quad-Head detection framework. To enhance multi-modal feature fusion, we introduce a Channel Swap SCDown Block (CSSB) for initial feature interaction and a lightweight Spatial Channel Attention Fusion Module (SCAFM) to further refine the integration of complementary RGB-IR features. To improve multi-scale feature utilization, we designed the Dynamic-Weight-based Quad-Head Detector (DWQH), which dynamically assigns weights to different scales, enabling adaptive fusion and enhancing multi-scale feature representation. This mechanism significantly improves detection performance, particularly for small objects. Furthermore, to ensure real-time applicability, we incorporate lightweight optimizations, including the Partial Cross-Stage Pyramid (PCSP) and SCDown modules, which reduce computational complexity while maintaining high detection accuracy. FQDNet was evaluated on three public RGB-IR datasets—M3FD, VEDAI, and LLVIP—achieving mAP@[0.5:0.95] gains of 4.4%, 3.5%, and 3.1% over the baseline, with only a 0.4 M increase in parameters and 5.5 GFLOPs overhead. Compared to state-of-the-art RGB-IR object detection algorithms, our method strikes a better balance between detection accuracy and computational efficiency while exhibiting strong robustness across diverse detection scenarios.