In view of fusion object detection of visible light image and infrared image, an object detection algorithm based on decisionlevel fusion is proposed. The YOLOv3 network is retrained by establishing a labeled data set, and the trained YOLOv3 network is used to detect the visible light image and the infrared image separately before fusion. In the process of fusion, a novel detection fusion algorithm is proposed. First, the accurate results of the object detected only in the visible image or only the infrared image are retained, and then the accurate results of the same object in the object detected in both of the visible image and the infrared image are weighted and fused. Finally, taking the combined detection results as the detection results of all corresponding objects in the fused image, the rapid object detection based on decisionlevel fusion is realized. The experimental results show that all indicators have a good performance on the established data set. Among them, the detection accuracy of the proposed algorithm amounts to 84.07 per cent. Compared with the algorithm of detecting the visible light images and the infrared images alone, the detection accuracy of the algorithm increases by 2.44% and 21.89% respectively, more objects can be detected, and there is a decrease in false detection of objects. Compared with the three detection algorithms based on featurelevel image fusion, the detection accuracy of the algorithm increases by 4.5%, 1.74 %, and 3.42% respectively.