Abstract: With the continuous innovation of target detection algorithms and UAV(Unmanned Aerial Vehicle) technology,detecting small targets in UAV aerial images has become a hot research topic.UAV aerial images have problems such as a high proportion of small targets,dense distribution,and complex environments.To address these issues,this paper proposes an improved UAV aerial image small target detection model MPI-YOLO based on YOLOv11n.Firstly,the MSFAM(Multi-Scale Feature Aggregation Module) replaces the first two C3k2 modules in the Backbone of YOLOv11n to enhance the feature extraction ability for small target information.Secondly,to solve the problem of a high proportion of small targets in UAV images,a new P2 small target detection layer is added,so that small targets can be better detected.Finally,IBDFPN(Improved Bidirectional Dense Feature Pyramid Network) is proposed,which extends the level range of the pyramid network and introduces skip connections to enhance the cross-layer interactions between shallow and deep features,achieving efficient multi-scale information fusion.The VisDrone2019 dataset is utilized for ablation experiments and comparative experiments.The ablation experiments verify the effectiveness of each improved strategy.The comparative experimental results show that MPI-YOLO outperforms other comparison mo-dels in small target detection accuracy.Compared with the benchmark model YOLOv11n,the mAP50 of MPI-YOLO increases by 9.1 percentage points.Generalization experiments are conducted on the UAVDT dataset and Tinyperson dataset,verifying its good generalization ability.

Key words: Small target detection, UAV aerial images, Feature pyramid network, Multi-scale feature fusion, Bidirectional feature fusion