Aiming at the problem that the multi-scale and small-scale of pedestrians in unmanned scenario causes the increase of missed detection rate and the decrease of detection accuracy， this paper proposes a pedestrian detection method that fuses deep and shallow layer features and cascade dynamic selection mechanism. Firstly， on the basis of YOLO v3-tiny， we improve the feature extraction part based on the densely connected convolutional neural network， and fuse the deep and shallow features of pedestrians to enhance the network’s ability to recognize pedestrians. Secondly， we cascade the attention module with dynamic selection mechanism on the improved backbone network to make the detection network more adaptable to dynamic pedestrian scale changes. Finally， we choose the BDD 100K dataset and the Caltech pedestrian dataset to conduct experiments. Under the premise of real-time performance （25 ms/sheet）， the missed detection rate of pedestrian is reduced by 11.4% and the average detection accuracy is improved by 11.7% in the BDD 100K dataset， and the missed detection rate of pedestrian is reduced by 10.1% and the average detection accuracy is improved by 6.7% in the Caltech dataset， which is suitable for unmanned pedestrian detection.