In order to improve the accuracy and speed of aerial image target detection, an improved CenterNet aerial image target detection algorithm based on adaptive threshold is proposed. The center point of the target is used as the key point to replace the anchor box for classification and boundary regression, and an adaptive threshold prediction branch is designed to screen and optimize the preprocessing results. At the same time, the encoding-decoding network structure is designed. Through the deformable cavity convolution structure and the convolutional block attention-connection structure based on the attention mechanism, shallow spatial information, and deep semantic information are effectively extracted and fused. In addition, data enhancement is realized by discarding structured information and building new samples with false and missing detection targets, so as to reduce false and missing detection rates. Experiments are performed on the open data set NWPU VHR-10, the results show that compared with CenterNet based on ResNet-50, mean average precision of proposed algorithm increased by 5.17%, and intersection of union of 0.50 and 0.75 are improved by 3.57% and 3.61%, respectively. The detection speed reaches 45 frame·s ^-1, achieving good detection accuracy and real-time balance.