In the field of target detection,in complex environments such as nighttime,heavy fog,occlusion and battlefield camouflage,using a single image sensor cannot obtain all the scene information,which makes it difficult to improve the accuracy of target detection in complex environments.In view of this,a BF-YOLOv5 algorithm based on YOLOv5 is proposed.The algorithm adopts a dual-branch structure.Visible images and infrared images are read through two Backbones,and CBAM is fused into each Backbone.The importance of each feature channel is automatically obtained by learning,and the obtained importance is used to enhance features and suppress those that are not important to the current task.BiFormer attention mechanism is integrated into the Neck section to improve the detection ability on small targets.The experiments show that the detection accuracy of BF-YOLOv5 algorithm on infrared and visible image dataset FLIR and LLVIP is higher than that of the original algorithm,and the mean Average Precision (mAP) on FLIR dataset is as high as 86.6%,which is 2.2 percentage points higher than that of the original dual-branch algorithm,and the detection performance on the fused infrared and visible images is significantly improved.