计算机技术与发展2026，Vol.36Issue(2)：71-77,7.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0249
基于YOLOv8n的无人机视角红外小目标检测算法
Infrared Small Target Detection Algorithm for UAV Perspective Based on YOLOv8n
摘要
Abstract
In infrared ground target detection from an unmanned aerial vehicle(UAV),small target detection is plagued by the problems of false detection and missed detection.To tackle this issue,we propose an improved YOLOv8n detection algorithm,which optimizes YOLOv8n in multiple aspects.Firstly,a 160×160 small target detection layer is added,and the feature map output by the first C2f module of the original network is introduced into the detection head to strengthen the detection of small-sized targets.Meanwhile,a dual-convolution CSP_BiFormer bottleneck module is incorporated to enhance the feature extraction capability and effectively handle the long-range dependencies between features.Secondly,the loss function is improved by combining EIoU and CIoU to optimize the adjustment of the length and width of the prediction box.Finally,a multi-scale feature adaptive attention module is introduced to fuse information of different scales,thereby improving the detection performance for both small and large targets.Experimental results on the HIT-UAV dataset show that compared with YOLOv8n,the improved algorithm achieves increases of 10.4 percentage points in precision,3.7 percentage points in recall,6.7 percentage points in mean average precision(mAP@0.5),5.4 percentage points in mAP@0.5:0.95,and 15.4 frames per second in FPS.Moreover,it exhibits better detection performance in complex scenarios compared with current main-stream algorithms,which verifies the effectiveness and good generalization ability of the proposed algorithm.关键词
小目标检测/无人机/红外图像/注意力机制/特征提取Key words
small target detection/UAV/infrared image/attention mechanism/feature extraction分类
信息技术与安全科学引用本文复制引用
刘奕阳,魏延..基于YOLOv8n的无人机视角红外小目标检测算法[J].计算机技术与发展,2026,36(2):71-77,7.基金项目
重庆市技术创新与应用发展重点项目(cstc2019jscx-mbdxX0061) （cstc2019jscx-mbdxX0061）
