As for the low aircraft detection rate by the traditional U-Net due to small aircraft targets, low resolution and complex background, a lightweight multi-scale attention U-Net model (LWMSAU-Net) is proposed. The model consists of encoding and decoding subnetworks corresponding to each other. The encoding subnetwork adopts multi-scale modules, and the residual jump connection module is used between the encoding and the corresponding decoding module to fuse the shallow features and deep features of the image, the more of the edge and fine structural features of the aircraft image is preserved by increasing the weight of shallow features and preserving. The last encoding module adopts residual attention connection module to connect encoding subnetwork and decoding subnetwork to strengthen the detection of small scale aircraft targets. The decoding path consists of 4 modules, where each deconvolution multiplies the size of the feature graph by 2, halving the number of feature graphs, and then combines with the feature graph of the symmetric encoding path. Compared with U-Net, the number of layers of LWMSAU-Net is decreased by 1. Experiments on remote sensing aircraft image dataset shows that the proposed method can effectively detect aircraft targets in remote sensing images with an accuracy of 94.72%.