HM-YOLO:融合多尺度特征的轻量化航拍图像检测算法OA

Aiming at the problems of tiny target size,complex background environment and difficult feature extraction in the UAV aerial image detection task,a lightweight aerial image detection algorithm HM-YOLO is proposed on the basis of YOLOv11.First,downsampling and upsampling of features of different scales in the backbone network extends the information interaction between different feature channels,and at the same time optimizes the shallow feature graph size to adapt to the tiny targets in aerial images;second,an efficient feature extraction module C3k2_MSEE is designed,which first divides the feature graph using adaptive mean pooling,and then highlights the edge information through the edge enhancement module to avoid the loss of feature information of the small targets in the deep network;then,a hierarchical attention fusion module HAFB is proposed,which strengthens the model's understanding of the contextual contextualiza-tion of the features in the backbone network by constructing a local and global dual-channel attention network,which strengthens the model's ability to integrate contextual information;finally,DyHead,a dynamic detection head with multi-ple attention mechanisms,is introduced to further optimize the ability to perceive small target feature information.And HM-YOLO is lightweighted using the LAMP pruning method and BCKD knowledge distillation strategy,which signifi-cantly compressees the volume of the model.Experimental results on the Visdrone2019 dataset demonstrate that the improved algorithm achieves 8.4,5.7,and 8.4 percentage points increases in accuracy,recall,and mAP@50,respectively,are able to effectively cope with the challenges in the task of target detection in UAV aerial images.