The motion parameters of warhead fragment group has important significance for evaluating the damage power of ammunition. Aiming at the problems of low fragment object detection precision caused by small fragment object size， complex background information， and few fragment samples， this paper proposed a YOLOv5-FD（You Only Look Once v5-Fragment Detection） method for warhead fragment group object detection. Firstly， a small object detection layer was added at the network output layer， changing the original three-scales to four-scales， and a lightweight upsampling module called CARAFE （Content Aware ReAssembly of FEatures） was introduced in the feature fusion network to replace the original nearest neighbor interpolation upsampling， reducing the loss of small object feature information and improving the ability to extract small fragments. Secondly， the CA （Coordinate Attention） module was introduced into the feature extraction network to enhance of fragment features， weaken background information， and suppress interference from complex backgrounds. Finally， the MAML （Model Agnostic Meta Learning） algorithm was introduced during the model training process to achieve high detection performance using only few-shot fragment datasets. The experimental results show that YOLOv5-FD algorithm achieves the precision of 90.5%， the recall rate of 85.4%， and the average precision of 88.2% in the self-made fragment datasets. Compared with the original YOLOv5s algorithm， it improved by 7.1%， 7.9%， and 7.5%， respectively， effectively improving the precision of fragment object detection.