The carrier-based aircrafts on the carrier deck are dense and occluded, so that the carrier-based aircraft targets are difficult to detect, and the detection effect is easily affected by the lighting condition and target size. Therefore, an improved Faster R-CNN (Faster Region with Convolutional Neural Network) carrier-based aircraft target detection method was proposed. In this method, a loss function with a repulsion loss strategy was designed, and combined with multi-scale training, pictures collected under laboratory condition were used to train and test the deep convolutional neural network. Test experiments show that compared with the original Faster R-CNN detection model, the improved model has a better detection effect on occluded aircraft targets, the recall increased by 7 percentage points, and the precision increased by 6 percentage points. The experimental results show that the proposed improved method can automatically and comprehensively extract the characteristics of carrier-based aircraft targets, solve the detection problem of occluded carrier-based aircraft targets, has the detection accuracy and speed which can meet the actual needs, and has strong adaptability and high robustness under different lighting conditions and target sizes.