Both infrared and visible images are widely used in the field of target detection; however, unimodal images find it difficult to satisfy the requirements of low-visibility road target detection. Therefore, this study proposes a low-visibility road target detection algorithm based on infrared-visible fusion from the perspective of bimodal fusion. First, the input images were pre-processed using various IR-visible dual-mode image fusion algorithms, five parameters, including mean, standard deviation, information entropy, mean gradient, and spatial frequency of the fused images, were quantitatively analyzed, and the detection model for low-visibility road targets was obtained by optimizing the training detection network. Finally, the accuracies of the algorithm and model were evaluated in terms of the model-training results and target detection results. The experimental results demonstrate that the false- and missed-detection rates of the model trained by the algorithm in this study were significantly reduced compared with other algorithms, and the detection accuracy was improved from 75.51% to 88.86% compared with the existing algorithm using unimodal images; in addition, the image processing speed satisfied the requirement for real-time detection.