UAV object detection can be used in anti-UAV scenarios.To facilitate algorithm deployment on embedded devices,a lightweight model is often required.YOLOv4-tiny,the lightweight version of the YOLOv4 object detection algorithm,has a fast detection speed with relatively simple network structure and low detection accuracy.In order to further improve the detection accuracy,the model of YOLO-L2 is proposed.The backbone network of YOLOv4-tiny is selected for feature extraction,and a path aggregation network based on coordinate attention is used for feature fusion.In the process of fusion,a set of learnable coefficients are used for weighting.A cascade residual module named ResBlock-L2 is embedded in the deepest feature layer to enlarge receptive fields and fuse features with different receptive fields.The bounding box loss function MEIoU is proposed to replace CIoU.Compared with YOLOv4-tiny,the improved algorithm improves the mAP by 3.19% and 3.95% respectively in VOC dataset and self-made UAV-L dataset, and meets the real-time requirements.