In optical remote sensing images, the aircraft to be detected is very small; external environmental factors such as cloud occlusion, aircraft, and the site background are easily fused; and the interference of objects to aircraft has a great impact on the aircraft characteristics in remote sensing images. In response to the above problems, we designed a remote sensing aircraft detection method based on deep learning. First, to ensure the feature extraction capability and limit the number of calculations of the network, the LightNet v2 network unit is designed, and it constitutes an efficient backbone network. In addition, spatial pyramid pooling of residual ideas (Res-SPP) is performed on the output results of the backbone network. Res-SPP is used to separate more important contextual features while using almost no computing space. A multi-scale fusion prediction network (MFPN) is proposed to perform feature fusion from multiple angles to achieve a rich combination of gradients. The MFPN enhances the network’s ability to detect extremely small objects and can improve accuracy while ensuring that the method is lightweight. Finally, according to the judgment of the threshold, the dark channel defogging method, which enhances the ability to detect aircraft in cloudy and foggy scenes, is used for remote sensing images full of clouds and fog. The experimental results show that the proposed method can detect airplanes, especially very small airplanes, in various scenarios. The amount of calculation of the method is 23.56 BN, the model volume is 56 MB, the speed on a GTX 1080 platform reaches 157 frames per second (FPS), and the F1  %   on the remote sensing aircraft data set reaches 99.2. In particular, this method can be embedded in an ordinary field programmable gate array platform due to its lightweight characteristics, and the calculation speed can reach 32 FPS.