辽宁工程技术大学学报（自然科学版）2026，Vol.45Issue(3)：349-358,10.DOI:10.11956/j.issn.1008-0562.20250493
基于改进YOLOv12的无人机图像小目标检测方法
Small target detection method of UAV image based on improved YOLOv12
摘要
Abstract
Aiming at the problems of low detection accuracy,strong background interference and weak feature expression ability of small targets in unmanned aerial vehicle(UAV)remote sensing images,a target detection algorithm combining multi-core feature extraction and triplet attention mechanism is proposed.Based on the YOLOv12 framework,the MogaNet dual-domain aggregation mechanism is introduced to enhance the multi-scale feature extraction ability,the coordinate attention(CA)module is embedded to improve the spatial positioning accuracy,and the triple attention mechanism is designed to jointly strengthen the attention to small targets in the spatial,channel and scale dimensions,and the CMUNeXt feature fusion strategy is combined to optimize cross-level information transmission.The research results show that the index ImAP@0.5 of the algorithm on the VisDrone2019 and NWPU VHR-10 datasets reaches 0.331 and 0.808,respectively,which is 5.7％and 15.0％higher than the YOLOv12 model,and significantly improves the detection accuracy and robustness of small targets in complex backgrounds.关键词
无人机遥感图像/小目标检测/三重注意力机制/YOLOv12模型/多尺度特征提取Key words
UAV remote sensing image/small target detection/triplet attention/YOLOv12 model/multi-scale feature extraction分类
信息技术与安全科学引用本文复制引用
任洪娥,张佳源,王金聪,郭继峰..基于改进YOLOv12的无人机图像小目标检测方法[J].辽宁工程技术大学学报（自然科学版）,2026,45(3):349-358,10.基金项目
国家自然科学基金项目(62466012) （62466012）
黑龙江省自然科学基金项目(LH2024F047) （LH2024F047）
