At present,most object detection rely on large-scale annotation datasets to ensure the accuracy of detection.In the actual scene,it is very difficult to obtain a large amount of data,and it also takes a lot of manpower and material resources to annotate the data.To solve the problem,a Few-Shot Object Detection algorithm based on Faster RCNN,CA-FSOD,is proposed,which detects the object samples when there are only a few annotated samples in the object category.In order to improve the detection performance,a CBAM-Attention-RPN module is proposed to reduce the number of irrelevant candidate regions.Secondly,a global-local relation detector module is proposed to obtain candidate regions that are more related to the object category by associating the features of a small number of annotated samples and samples to be detected;Finally,a classifier based on cosine Softmax loss is proposed as the classification branch of object detection, which can effectively aggregate the same category features,reduce the intra-class variance and improve the detection accuracy.In order to verify the proposed algorithm,it is trained and tested on MS COCO dataset.The experimental results show that the AP50 is 21.9% for this method,which is better than that of some existing few-shot object detection algorithms.