改进YOLOv11的无人机小目标检测算法

doi:10.3778/j.issn.1002-8331.2503-0274

摘要/Abstract

摘要： 针对无人机小目标检测任务中小目标像素少、尺度变化大、易受背景干扰的问题，提出一种基于YOLOv11的改进算法。设计新的ELAN-DC模块改进主干网络，在高效层聚合网络ELAN的CBS模块中结合双卷积DC，增强模型主干部分的特征提取能力。设计一种新的全局到局部双向特征融合结构GLBiFPN，提升多尺度特征融合的效果。引入动态检测头DyHead，进一步增强模型的检测精度。实验结果表明，在VisDrone2019数据集上，改进算法的检测精度mAP50和mAP50-95相比YOLOv11n分别提高5.1和3.5个百分点。

关键词: YOLOv11, 小目标, 多尺度特征融合, 无人机

Abstract: To address the issues of small target detection tasks for unmanned aerial vehicles (UAVs), such as few pixels, large scale variations, and susceptibility to background interference, an improved algorithm based on YOLOv11 is proposed. Firstly, a new ELAN-DC module is designed to improve the backbone network, combining double convolution DC in the CBS module of the efficient layer aggregation network ELAN to enhance the feature extraction capability of backbone part of the model. Secondly, a new global-to-local bidirectional feature fusion structure GLBiFPN is designed to improve the effect of multi-scale feature fusion. Finally, a dynamic detection head DyHead is introduced to further enhance the detection accuracy of the model. Experimental results show that on the VisDrone2019 dataset, the detection accuracy, mAP50 and mAP50-95, of the proposed algorithm has increased by 5.1 and 3.5 percentage points respectively, compared to YOLOv11n.

Key words: YOLOv11, small target, multi-scale feature fusion, unmanned aerial vehicles (UAVs)

刘玉萍, 尚翠娟, 李明明. 改进YOLOv11的无人机小目标检测算法[J]. 计算机工程与应用, 2025, 61(15): 124-131.

LIU Yuping, SHANG Cuijuan, LI Mingming. Improved YOLOv11 Algorithm for Small Target Detection in UAVs[J]. Computer Engineering and Applications, 2025, 61(15): 124-131.

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