A pedestrian detection network based on the weight learning of fusing multimodal information was developed to address the issues of the pedestrian detection method based on infrared and visible modal fusion in adapting to changes in the external environment. First, unlike the fusion method used in several recent studies in which two modalities are stacked directly, the weight learning fusion network reflects dif. ferent contributions of the modalities to the pedestrian detection task under different environmental condi. tions. The differences between the two modalities were determined through dual-stream interaction learn. ing. Next, based on the current characteristics of each modal feature, the weight learning fusion network assigned the corresponding weights to each modal feature to generate the fusion feature by performing weighted fusion autonomously. Finally, a new feature pyramid based on the fusion feature was generated, and previous information about the pedestrian was improved by changing the size and density of prior boxes to complete the pedestrian detection task. The experimental results indicated that the log-average miss rate of the Kaist multispectral pedestrian detection dataset reached 26. 96%, which was 2. 77% and 27. 84% lower than that of the direct stacking method and baseline method, respectively. The adaptive weight fu. sion of infrared and visible modal information could effectively be used to obtain complementary modal in. formation to adapt to external environmental changes and significantly improve pedestrian detection perfor. mance.