As the current pedestrian detection method has the problems of large computation and low detection accuracy, an improved pedestrian detection method based on YOLOv4-Tiny was proposed. This method introduces Convolutional Block Attention Module into CSPDarknet53-tiny network to get richer features by learning the position information and channel information of the image, adds the spatial pyramid pooling module following CSPDarknet53-tiny, which can greatly increase the receptive field and isolate the most significant context features, and uses CIoU loss function to optimize the combined loss of multiple tasks. In the experiment, the training set in INRIA and WiderPerson are used as the training model, and the test set in INRIA and WiderPerson are used as the test set to verify the model. Compared with YOLOv4-Tiny, the precision, recall and average accuracy of the improved YOLOv4-Tiny network in INRIA test set are increased by 6.23%, 3.15% and 6.12%, respectively, and the improved network increased the precision, recall, and average accuracy in the WiderPerson test set by 3.65%, 3.28%, and 4.41%, respectively. It is found that this improved model can extract pedestrian features more easily and improve the detection accuracy on the premise that the real-time detection is hardly affected.