Pedestrian detection in complex environments presents a critical technical challenge for autonomous driving systems. This paper proposes a pedestrian target detection method based on multimodal feature fusion of infrared and visible light images. First, to address the challenge of cross-modal feature fusion, we propose an infrared-visible image fusion algorithm (DIVIF) based on a multi-source diffusion model. The algorithm incorporates a reversible feature decoupling module (IGCM) to capture target information distributions across modalities and extract large-scale structural features, along with a lightweight local capture module (LLCM) to enhance detail fidelity through attention-driven dynamic interaction. A global-local iterative optimization mechanism is introduced to achieve effective feature transfer and deep fusion. Second, for low-illumination scenarios, we propose an improved YOLOv8n detection network with lightweight architecture and attention mechanisms. The model integrates MobileNetV4 and GSConv into the backbone and neck networks for efficiency, while incorporating EMA attention to strengthen key feature extraction and MPDIoU loss to optimize convergence. Furthermore, a complete pedestrian detection system implementation is designed for practical applications. Experimental results demonstrate that the proposed method effectively fuses multimodal features, achieving precise detection in low-light and occluded scenarios. Compared to mainstream detection networks, our algorithm outperforms in all evaluation metrics.