Concerning the problem of low object detection precision of DEtection TRansformer （DETR） for small targets， an object detection algorithm with optimized feature extraction structure， called CF?DETR （DETR combined CSP?Darknet53 and Feature pyramid network）， was proposed on the basis of DETR. Firstly， CSP?Darknet53 combined with the optimized Cross Stage Partial （CSP） network was used to extract the features of the original image， and feature maps of 4 scales were output. Secondly， the Feature Pyramid Network （FPN） was used to splice and fuse the 4 scale feature maps after down?sampling and up?sampling， and output a 52×52 size feature map. Finally， the obtained feature map and the location coding information were combined and input into the Transformer to obtain the feature sequence. Through the Forward Feedback Networks （FFNs） as the prediction head， the category and location information of the prediction object was output. On COCO2017 dataset， compared with DETR， CF?DETR has the number of model hyperparameters reduced by 2×10⁶， the average detection precision of small objects improved by 2.1 percentage points， and the average detection precision of medium? and large?sized objects improved by 2.3 percentage points. Experimental results show that the optimized feature extraction structure can effectively improve the DETR detection precision while reducing the number of model hyperparameters.