To address the challenges of low detection accuracy and high false detection rates of small targets by drones, a dual-modal image fusion method has been proposed. This paper outlines a framework for fusing infrared and visible images using front-end, mid-end, and back-end strategies. A dual-channel image fusion detection method has been developed based on the back-end fusion strategy and the YOLOv8 object detection framework. This method constructs a dual-channel feature fusion block to integrate features from both infrared and visible images. It also incorporates a BRA (bi-level routing attention) module into the neck network layer to improve the model ability to detect small targets. Experimental results show that the proposed method increases the mean average precision (mAP) by 14.78% and 12.99% compared to using single infrared and visible images with YOLOv8 on the DroneVehicle dataset. Additionally, the mAP of the proposed method increased by 7.1% compared to the PSFusion fusion detection method on the same dataset.