基于改进YOLOv12的无人机图像小目标检测方法OA
Small target detection method of UAV image based on improved YOLOv12
针对无人机遥感图像中小目标检测精度低、背景干扰强、特征表达能力弱等问题,提出一种融合多核特征提取与三重注意力机制的目标检测算法.在YOLOv12框架上,引入MogaNet双域聚合机制增强多尺度特征提取能力,嵌入CA注意力模块提升空间定位精度,并设计三重注意力机制,在空间、通道与尺度维度联合强化小目标关注度,结合CMUNeXt特征融合策略优化跨层级信息传递.研究结果表明:该算法在VisDrone2019和NWPU VHR-10数据集上的ImAP@0.5分别达到0.331和0.808,较YOLOv12模型分别提升5.7％和15.0％,显著改善了复杂背景下小目标的检测精度与鲁棒性.
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.
任洪娥;张佳源;王金聪;郭继峰
东北林业大学 计算机与控制工程学院,黑龙江 哈尔滨 150040||黑龙江省林业智能装备工程研究中心,黑龙江 哈尔滨 150040东北林业大学 计算机与控制工程学院,黑龙江 哈尔滨 150040东北林业大学 计算机与控制工程学院,黑龙江 哈尔滨 150040||黑龙江省林业智能装备工程研究中心,黑龙江 哈尔滨 150040桂林航天工业学院 计算机科学与工程学院,广西 桂林 541000
信息技术与安全科学
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
UAV remote sensing imagesmall target detectiontriplet attentionYOLOv12 modelmulti-scale feature extraction
《辽宁工程技术大学学报（自然科学版）》 2026 (3)
349-358,10
国家自然科学基金项目(62466012)黑龙江省自然科学基金项目(LH2024F047)
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