红外技术2026，Vol.48Issue(3)：298-304,7.
基于多尺度注意力的无人机红外图像目标检测方法
Unmanned Aerial Vehicle Infrared Image Target Detection Method Based on Multi-Scale Attention
摘要
Abstract
Infrared images captured by drones contain limited textural information and weak edge features,making it difficult for lightweight networks to improve the detection accuracy of small-scale targets in infra-red images.Based on the YOLOv8N network,the last layer of the original network was first trimmed to re-duce the number of network parameters and improve the loss of small target detail features caused by the deep convolutional neural network.Subsequently,based on the efficient multi-scale attention(EMA)of cross-spatial learning,the feature-extraction module C2f-EMA was constructed,which retained and high-lighted the small target features to the greatest extent while suppressing the interference of the background environment through channel remodeling and dimensional grouping.Finally,the WIoU loss function was in-troduced to replace the original network CIoU loss function and realize the dynamic adjustment of the target weight to further improve the detection performance of the network for small targets.The experimental re-sults show that compared with other advanced networks,such as YOLOv8n and PiCoDet on the HIT-UAV dataset,UIDNet has a smaller model size and better detection effect than the original YOLOv8n model.The average detection accuracy of UIDNet was increased by 1.7%,the number of parameters was reduced by 67.4%,and the model volume was compressed by 63.5%to only 2.3 MB.关键词
红外图像目标检测/YOLOv8/多尺度注意力/WIoU损失函数Key words
infrared image target detection/YOLOv8/multi-scale attention/WIoU loss function分类
信息技术与安全科学引用本文复制引用
朱磊,赵兴瑞,李光健..基于多尺度注意力的无人机红外图像目标检测方法[J].红外技术,2026,48(3):298-304,7.基金项目
国家自然科学基金(61971339). （61971339）
