Aiming at the problem of difficult and limited sample acquisition in SAR(synthetic aperture radar) image target detection, a learning model of joint GAN (generative adversarial network) and detection network was proposed. The original training set was used to pretrain the specially designed faster regional convolutional neural network. The deep convolutional GAN based on attention mechanism was employed to generate extensive synthetic samples, which were input into the detection network for prediction. The corresponding annotation information of the new samples was determined by the prediction information and probability equivalent class label allocation strategy, and the annotated new samples were used to expand the original dataset with a certain proportion. The detection network was retrained with the expanded dataset. Simulation results show that the proposed framework can improve the detection efficiency and performance of the network effectively.