A dual-branch feature enhancement and fusion backbone network (DBEF-Net) is proposed for object detection to address the challenges of infrared and visible bimodal object detection in complex dynamic environments. Specifically, DBEF-Net addresses issues such as insufficient object feature expression and the inability of infrared and visible features to fully utilize the complementary features in bimodal fusion leading to omission and misdetection. To further address the insufficient attention of the model to infrared and visible light features, a feature interaction enhancement module is designed to effectively focus on and enhance the useful information in bimodal features. A transformer-based bimodal fusion network is further adopted. To utilize the complementary features of bimodal modalities more effectively, a cross-attention mechanism is introduced to achieve deep fusion between the modalities. Experimental results show that the proposed method has higher average detection accuracy than existing bimodal object detection algorithms on the SYUGV dataset, meeting the processing speed for real-time detection.