Abstract: Since the original YOLOv3 model takes up a large amount of storage space， more initialization dataset samples and parameters are required. A deep learning object detection compression model YOLOv3-ADS was proposed based on YOLOv3. The proposed model uses the methods of splicing and stacking for data enhancement of the fewer representative initial datasets. It introduces the DIoU loss function， and improves the accuracy of object detection. Finally， YOLOv3-ADS model was compressed by sparse training and pruning rate threshold setting， which reduces the number of redundant nodes， parameters and the required storage space. The experimental results show that， compared with the existing YOLOv3 model， the average accuracy of the proposed YOLOv3-ADS compression model is increased by about 30%， from 0.6418 to 0.8368， and the number of parameters is reduced by 96.6%， from the original 63.0MB to 2.2MB. At the same time， the storage space required by YOLOv3-ADS model is reduced by 96.5%， from 252MB to only 8.81MB.

Key words: object detection; YOLOv3-ADS model;deep learning;YOLOv3 model;compression model