计算机工程与应用2025，Vol.61Issue(10)：267-278,12.DOI:10.3778/j.issn.1002-8331.2407-0401
双向多尺度特征融合的无人机检测算法
UAV Detection Algorithm Based on Bidirectional Multi-Scale Feature Fusion
摘要
Abstract
A UAV target detection algorithm with improved YOLOv8n is proposed to address the difficulty of recognizing"black-flying"UAV targets in complex environments such as parks,schools,and airports,where targets vary in scale,are blurred,and occluded.The C2f-RVB module is designed by fusing the RepViTBlock structure and efficient multi-scale attention(EMA)to improve the Bottleneck block in the C2f module,enhancing the multi-scale feature extraction capa-bilities of the model with fewer parameters.Furthermore,a dynamic boundary fusion module DBFFPN(dynamic bound-ary fusion feature pyramid network)is constructed in the neck feature fusion network,and a new small target detection layer is added to aggregate shallow boundary information and deep semantic information,thereby enhancing the ability of the model to detect obscuration.In the loss function section,MFShape-IoU is proposed as a replacement for the original model CIoU,encourages the model to focus on the shape and scale of bounding box,while aggregating challenging sam-ples,ultimately improving target localization accuracy.Experiments are conducted on the public dataset CBD,and the results demonstrate that the enhanced algorithm exhibits improvements ranging from 4.1 percentage points to 93.0%on mAP@0.5,and 4.2 percentage points to 57.1%on mAP@0.5:0.95 in comparison to YOLOv8n.Meanwhile,the algorithm exhibits higher accuracy compared to YOLOv8s and significantly lower complexity,meets the needs of deployment on mobile devices.关键词
复杂背景/反无人机/小目标检测/YOLOv8/注意力机制Key words
complex background/anti-drone/small target detection/YOLOv8/attention mechanism分类
信息技术与安全科学引用本文复制引用
汤栎,贾渊,张玉宁..双向多尺度特征融合的无人机检测算法[J].计算机工程与应用,2025,61(10):267-278,12.基金项目
四川省自然科学基金(2023NSFSC1417) （2023NSFSC1417）
西南科技大学博士基金(22zx7174). （22zx7174）
