Different from other scale objects,small objects have the characteristics of carrying less semantic information and a small number of training samples.Therefore,the current object detection algorithm has the problem of low detection accuracy for small objects.Aiming at this problem,a Transformer object detection algorithm based on multi-granularity is proposed.Firstly,adopting the multi-granularity idea,a new Transformer serialization method is designed to predict the object position granularly from coarse to fine,thereby improving the object location effect of the model.Then,based on the three-way decision idea,fine-grained mining of small object samples and regular-scale object samples increases the number of small object samples and hardnegative samples.Finally,experimental results on the COCO dataset show that,the small object detection average accuracy(APs) of the algorithm reaches 31.5%,and the mean average accuracy(mAP) reaches 49.1%.Compared with the baseline model,the APs is improved by 1.4% and the mAP is improved by 2.2%.The algorithm effectively improves the detection effect of small objects and significantly improves the overall accuracy of object detection.