现代信息科技2025，Vol.9Issue(8)：46-53,60,9.DOI:10.19850/j.cnki.2096-4706.2025.08.010
YOLO-IRLight:基于YOLOv8的轻量级无人机红外小目标检测算法
YOLO-IRLight:Lightweight UAV Infrared Small Target Detection Algorithm Based on YOLOv8
倪梦琪 1陈凯源1
作者信息
- 1. 华北水利水电大学,河南 郑州 450046
- 折叠
摘要
Abstract
To address the issues of low detection accuracy and high computational load in infrared small target detection from UAV aerial perspectives,a lightweight infrared small target detection model,YOLO-IRLight,is proposed based on YOLOv8s.This model introduces the EMA(Efficient Multiscale Attention)Attention Mechanism to enhance feature extraction capabilities.A PConv-C2f module is added to the neck of the network to reduce computational load and fuse scale sequence features,and a P2 detection layer is incorporated to optimize the network structure,thereby improving small target detection performance.A novel lightweight detection head,Group-Detect,is designed,and the NWD(Normalized Gaussian Wasserstein Distance)loss function is incorporated into the loss function of the model in a linear combination manner.Experimental results on the open dataset show that compared to the original YOLOv8s,the proposed model improves detection accuracy(mAP@0.5)by 1.7%,reduces the number of parameters by 45.9%,decreases computational complexity(GFLOPs)by 33.5%,and increases F1 score by 0.9%.The improved algorithm significantly outperforms traditional algorithms,with notable improvements in detection accuracy compared to current mainstream algorithms.关键词
小目标检测/红外目标/轻量化/YOLOv8/网络优化Key words
Small Target Detection/infrared target/lightweight/YOLOv8/network optimization分类
信息技术与安全科学引用本文复制引用
倪梦琪,陈凯源..YOLO-IRLight:基于YOLOv8的轻量级无人机红外小目标检测算法[J].现代信息科技,2025,9(8):46-53,60,9.
