Currently， pedestrian detection in multiple scenes is a research hotspot in the field of computer vision. Deep learning has attracted considerable attention and can provide high detection accuracy； however， the subsequent high-complexity operations seriously limit its application on mobile platforms. To address this problem， this paper proposes a lightweight pedestrian detection algorithm for multiple scenes. Firstly， a deep and shallow feature fusion network is constructed to learn the texture features of multi-scale pedestrians. Secondly， a cross-dimensional feature-guided attention module is designed to retain the interactive information between channels and spaces in the process of feature extraction. Finally， the redundant channels in the model are trimmed using the pruning strategy， to reduce the algorithm complexity. In addition， an adaptive Gamma correction algorithm is designed to reduce the influence of external disturbances， such as illumination and shadows， on the detection results of multiple scenes. The experimental results show that the proposed method can compress the model volume to 10 MB， and the processing speed can reach 93 Frame/s while achieving similar detection accuracy， which outperforms the current mainstream methods.