MAFS-YOLO:基于改进YOLO11的无人机视角小目标检测算法OA

To address the critical challenges in unmanned aerial vehicle(UAV)aerial imagery such as difficulty in detecting small objects,significant background interference,and multi-scale object variations,an enhanced object detection model named multi-branch attention and Fourier-based SPPF YOLO(MAFS-YOLO)is proposed based on the YOLO11 architecture.During the feature extraction stage,multi-branch attention network with wavelet enhancement module(MANet)is introduced,which decomposes input features into four frequency bands via wavelet transform and integrates parallel convolutional pathways to achieve multi-scale fusion of spatial details and frequency-domain structures.This design notably strengthens the perception of local textures and contours of small objects.In the backbone network,a Fourier-based spatial pyramid pooling fast(FourierSPPF)module is designed,leveraging fast Fourier transform to map features into the frequency domain.Through low-,medium-,and high-frequency masks,information from different frequency bands is separated and reconstructed before being restored to the spatial domain via inverse transform,thereby enhancing the ability of the model to capture global image structures and contextual relationships.A shape-decoupled IoU(SDIoU)regression strategy is employed in the loss function,where the matching of bounding box width and height is decoupled into two independent loss terms,improving the adaptability of the model to targets of varying scales.Experimental results on the VisDrone-DET2019 dataset demonstrate that MAFS-YOLO outperforms the baseline YOLO11 model,achieving improvements of 3.2,1.9,and 1.6 percentage points in precision,mAP@0.5,and mAP@0.5:0.95,respectively,while reducing the number of parameters by approximately 13%.In generalization experiments on the TinyPerson dataset,corresponding gains of 3.5,1.7,and 1.5 percentage points are observed.The experiments confirm that MAFS-YOLO significantly enhances the recognition accuracy of small objects while maintaining detection efficiency,providing a reliable technical pathway for object detection in complex UAV scenarios.