Abstract: Over the past 30 years, deep learning has sprung up. It has made remarkable progress in various computer vision tasks, coupled with the emergence of a large number of high-quality and diversified data, making a variety of data-dependent target detection methods reappear in the dawn. However, these deep network algorithms usually require a large amount of data to participate in the calculation of tens of billions of parameters, which is inefficient and requires higher and higher storage space, making it impossible to embed such a large network in small devices or mobile terminals. Therefore, this paper proposes an optimized target detection algorithm to adapt to the mobile environment. Based on the diversity and separability of CNN convolution kernels and the theory of Depthwise Separable Convolution structure, a One-Stage End-to-End object detection compression network DW-YOLOv3 is proposed. Finally, the results on VisDrone 2018 data set with detailed annotation show that the improved one-stage separable convolutional target detection compression network algorithm proposed in this paper can compress the network parameters 8-9 times, and improve the recognition performance of small target objects while having little impact on the overall performance of the network.