Remote sensing target detection algorithms based on deep learning have redundant parameters, large computation amount and poor real-time detection performance.To solve the above problems, a real-time remote sensing target detection algorithm based on depth separable convolution is proposed.The datasets are analyzed by anchor box (Anchor) clustering via the K-means++ algorithm, making the anchor box parameters more compliant with the remote sensing detection scenario.In order to reduce the quantity of model parameters and improve the detection speed, feature extraction is performed with the lightweight network MobileNetv3 as the backbone network.In addition, the design of PANet (Path Aggregation Network) structure based on depth separable convolution makes the quantity of network parameters further reduced.The quantity of model parameters after improvement is only 18.3% of the original, and the detection speed is 2.19 times faster than the original.Tests are conducted on three remote sensing datasets, that is, UCAS_AOD, RSOD and DIOR, and the experimental results show that the algorithm is robust and can effectively improve the real-time detection performance while ensuring the model detection accuracy.