In order to solve the problems of traditional airplane detection methods, such as low accuracy, high false alarm rate, and low speed, we propose a fast airplane detection method based on multi-layer feature fusion in a fully convolutional neural network. Firstly, we sample the shallow and deep features separately and fuse them at the same scale, which can alleviate the problem that the deep features are too sparse to express the small-size objects. Secondly, we redesign the size of the reference boxes to adjust to the practical size of the airplane in the input image. Thirdly, we replace the fully connected layers by convolutional layers to reduce the network parameters and adapt to input images with different sizes. Fourthly, we multiplex the convolutional layers and the learning-feature parameters of the proposal network and the detection network to improve the detection efficiency. The simulation results show that compared with typical airplane detection methods, the proposed method achieves higher accuracy and lower false alarm rate and greatly accelerates the detection speed.