To improve the performance of visible and infrared image decision-making-level fusion target detection algorithms, a decision-level fusion strategy based on model reliability was proposed. First, image preprocessing technology was adopted to improve the overall quality of an infrared image, and then visible and thermal infrared target detection models were trained and tested. The parameters required for the fusion strategy were obtained based on the model test results. The model detection results were fused according to the proposed fusion strategy, and the final fusion detection results were obtained. The experimental results showed that compared with the detection results of a single-target detection model, the missed detection rate of the fusion algorithm used in the daytime was 8.16% lower than that of the visible detection model, and the missed detection rate at night was 9.85% lower than that of the infrared detection model.