Object detection is one of the basic tasks of computer vision,and its main task is to classify and locate the targets in the image.The purpose of few-shot object detection is to use a very small number of training samples to achieve the detection ability of the objects,so as to reduce the complicated annotation work,and realize the application in the scenarios with only a small number of samples.The existing methods for few-shot object detection mainly include siamese neural network-based methods and fine-tuning-based methods,which enable models to achieve the classification and localization ability of few-shot categories by using the existing base-class datasets containing a large number of samples and few-shot datasets containing a small number of samples.The two-branch few-shot object detection method based on siamese neural networks was focused on,and fine-tuning-based few-shot object detection schemes were briefly introduced.The advantages and disadvantages of these schemes were analyzed.It is pointed out that the existing small-sample target detection scheme is not mature,the precision of the model needs to be improved and the performance evaluation scheme needs to be improved.However,the small-sample target detection scheme has a very broad application prospect,and in the future,the existing problems of few-short object detection will be solved by in-depth research,so that its accuracy can catch up with the traditional target detection.