改进YOLOv11s的航拍小目标检测方法研究OA

Aiming at the challenges of high-dynamic scenarios,limited target feature information,and constrained onboard computational resources in small object detection from UAV aerial images,this paper proposes a detection method for small aerial objects based on an improved YOLOv11s.Firstly,a dense cross-path feature pyramid network(DCP-FPN)is introduced as the Neck structure,where the large-object detection layer is removed and a small-object detection layer is added.By adopting dense connections to maximize feature utilization efficiency,the model significantly reduces the num-ber of parameters while effectively enhancing the capability to capture small object features.Secondly,a novel light-weight convolutional module named Ghost-DSConv is proposed,which integrates dynamic channel adjustment and dual-branch feature interaction.The main branch employs depthwise separable convolution(DSConv),supplemented by a low-cost branch composed of group convolutions to generate supplementary features.This design extracts spatial features at a lower computational cost and enhances feature diversity.Finally,the paper proposes a lightweight small-target attention mechanism module(lightweight small-target attention,LSTA),which introduces a triple attention reinforcement mecha-nism to significantly enhance the model's detection performance for small targets with minimal computational overhead.The experimental results on the VisDrone2019-DET dataset demonstrate that the improved algorithm proposed in this paper achieves 44.8%mAP50 and 27.4%mAP50-95.The enhanced YOLOv11s model has only 3.51×106 parameters and is deployed on an RK3588s-based embedded platform,where it successfully meets real-time detection requirements,achieving a favorable balance between accuracy and efficiency.Additionally,the proposed algorithm's strong generaliza-tion capability and effectiveness are further validated on the CARPK and Tinyperson datasets.