In order to improve the localization accuracy of object detection algorithm, a decision-level fusion strategy based on infrared image and visible image quality is proposed. An object detection framework based on yolo-v3 model is established to detect infrared images and visible images obtained in the same scene respectively. To address the issue where the center point of a detected target may deviate from its true position due to poor image quality or insufficiently distinct target features, the gradient amplitude mean and Laplacian Operator variance are used as indicators to evaluate the image quality, and the fusion weight is assigned. The simulation results show that the proposed algorithm can fuse infrared image and visible image target detection results to improve object positioning accuracy. Compared with the detection results of a single target detection model, the fusion results adopted are 45.57~46.19% higher than the infrared image detection results, and 23.91~24.78% higher than the visible light detection results.