In order to solve the problem of poor detection accuracy of CenterNet in remote sensing aircraft with dense targets and complex backgrounds, an improved memory CenterNet object detection algorithm is proposed. First, the multiscale receptive field module and residual attention module are introduced to improve ResNet50, which is used as a new backbone feature extraction network. It has a strong feature extraction ability and detection speed at the same time. Different dilated ratios are used in the multiscale receptive field to expand the receptive field and extract multiscale features, and the multiscale target features are highlighted through the attention mechanism. Then the last set of residual networks in ResNet50 is deleted, and the residual attention network is introduced and stacked in the same way. The feature parameters of different channels in the residual attention network are calculated by two-dimensional cosine transform and weighted with the original feature map, and the target features are highlighted to enhance the ability to locate the target at the end of the network. Second, three times deconvolution is replaced by a memory feature fusion module. The shallow detail information and the deep semantic information are fully fused. The key information in the output feature map is further enriched, which is beneficial to the improvement of model detection performance. Finally, the experimental results show that the average accuracy of remote sensing aircraft dataset is 88.46%, which is 18.21% higher than that of the original algorithm and effectively improves the detection accuracy of remote sensing aircraft targets.