The intelligent application of unmanned aerial vehicles （UAVs） is a hot topic in current research. To achieve efficient and real-time air-to-ground target detection for UAVs， this paper proposes a lightweight MBCF-YOLO model for air-to-ground target detection. First， a deep separable convolution to redesign an efficient and lightweight backbone network is introduced. Then， a small target detection branch with embedded recursive attention mechanism is added to the neck network to improve the detection accuracy of small targets and the ability of feature fusion. Finally， the focus loss function is combined with loss rank mining to improve the original loss function to overcome the data imbalance problem of air-to-ground datasets. Several control experiments and real machine applications have been conducted on this algorithm model， and the results show that compared with other current algorithms， the MBCF-YOLO algorithm achieves a better balance between accuracy and latency on the VisDrone 2021 dataset.