计算机工程与应用2024，Vol.60Issue(17)：191-202,12.DOI:10.3778/j.issn.1002-8331.2403-0256
AEM-YOLOv8s:无人机航拍图像的小目标检测
AEM-YOLOv8s:Small Target Detection Algorithm for UAV Aerial Images
摘要
Abstract
The AEM-YOLOv8s algorithm is proposed to address issues of low performance,missed detections,occlu-sions,and high model parameter count in small object detection in current UAV aerial imagery.Within the C2f module,the advantages of AKConv(alterable kernel convolution)and EMA(efficient multi-scale attention)are combined to design the C2f-BE module,which enhances the algorithm's ability to process features while reducing the model parameter count.By introducing a small object detection layer and BiFPN structure,through cross-scale connections and weighted feature fusion,more shallow features are retained,reducing algorithm parameters.The design of a multi-scale feature fusion branch merges shallow features containing more small object information with deeper semantic features,reducing missed detections under occlusion and improving small object detection performance.Experimental results on the VisDrone2019 public dataset demonstrate that the AEM-YOLOv8s algorithm achieves an mAP50 of 50.1%and mAP50:95 of 31.1%,representing respective improvements of 10.8 and 7.6 percentage points over YOLOv8s,while also reducing parameters by 32.2%compared to YOLOv8s.关键词
YOLOv8s/C2f-BE模块/小目标/多尺度Key words
YOLOv8s/C2f-BE module/small object/multi-scale分类
信息技术与安全科学引用本文复制引用
蒋伟,王万虎,杨俊杰..AEM-YOLOv8s:无人机航拍图像的小目标检测[J].计算机工程与应用,2024,60(17):191-202,12.基金项目
国家自然科学基金(61401269). （61401269）
