基于特征信息补充与增强的无人机视角图像目标检测算法OA

To address the challenges of significant object scale variation,a high proportion of small-sized objects,and severe background noise interference in images captured from unmanned aerial vehicle(UAV)viewpoint images,a novel object detection algorithm based on feature information enhancement and complementation is proposed.First,to leverage high-level semantic information for capturing richer multi-scale features,a multivariate fusion spatial pyramid pooling-fast(MFSPPF)method is introduced.Second,in order to avoid losing important information during feature fusion,a multi-branch semantic enhancement(MBSE)module is created to extract rich multi-scale features over several branches and create links between these features.In addition,the detailed feature complementation(DFC)module is proposed to extract and refine the low-level feature information to obtain rich,fine-grained feature information and achieve the complementation of the detailed information in the high-level features after feature fusion.Experiments conducted on the VisDrone2021 dataset demonstrate that the proposed algorithm outperforms the baseline YOLOv8m method,with average precision(AP),AP50,AP75,AP(s),AP(m),and AP(l)improving by 3.7%,5.4%,3.9%,3.1%,4.0%,and 7.9%,respectively.Furthermore,the proposed method is applicable to other YOLOv8 models.The proposed algorithm is appropriate for UAV perspective image detection jobs because it maintains a fast detection speed while achieving better detection performance across a range of intersection over union(IOU)thresholds and object sizes.