Aiming at the detection accuracy and speed of pedestrian detection in complex road environment,a lightweight pedestrian detection algorithm based on multi-scale information and cross-dimensional feature guidance is proposed.Firstly,based on the high-performance detector YOLOX,a multi-scale lightweight convolution is constructed and embedded in the backbone network to obtain multi-scale feature information. Secondly, an end-to-end lightweight feature guided attention module is designed,which guides the model to focus on the visible region of pedestrian targets by fusing spatial information and related information through cross-dimensional channel weighting mehod. Finally,in order to reduce the loss of feature information in the process of lightweight of the model,a feature fusion network is constructed by depthwise separable convolution with increasing the depth of the receptive field.The experimental results show that compared with other mainstream detection algorithms,the proposed algorithm on the KITTI dataset reaches 71.03% detection accuracy and 80 FPS detection speed,which has better robustness and real-time performance in scenes with complex background,dense occlusion and different scales.