Considering that YOLOv3 is not ideal for small and medium targets detection, an improved algorithm DX-YOLO is proposed. Firstly, the feature extraction network of YOLOv3 called Darknet-53 is improved, and the original residual module is replaced by ResneXt residual module, which optimizes the structure of convolution network. Inspired by Densenet, dense connection is introduced into Darknet-53 to realize feature reuse and improve the efficiency of feature extraction. According to the characteristics of data set, K-means algorithm is used to cluster the dimensions of data set to get the appropriate anchor box. Experiments on Udacity data set show that compared with YOLOv3, DX-YOLO algorithm improves the mAP by 3.42%; especially, the AP on medium and small targets increases by 2.74% and 5.98% respectively.