计算机工程与应用2026，Vol.62Issue(10)：111-122,12.DOI:10.3778/j.issn.1002-8331.2509-0361
Harmony-YOLO11:基于高频增强与特征引导的轻量级小目标检测算法
Harmony-YOLO11:Lightweight Small Object Detector via High-Frequency Enhancement and Feature Guidance
摘要
Abstract
Small object detection in drone-captured images faces challenges including scale imbalance,limited visual information,and constrained computational resources.Existing detection models often struggle to balance accuracy and complexity for this task.To address these issues,this paper proposes Harmony-YOLO11,an improved YOLO11n-based algorithm for drone-captured small object detection.It introduces a high-frequency enhancement and cross-scale adaptive module(HCM)to strengthen small object edges and focus on object regions,enhancing model adaptability.An efficient feature guide feature pyramid network(FG-FPN)is developed with a feature fusion guide block(FFGB),achieving light-weight and effective feature fusion through simplified network paths.The C3K2_CCA module integrates CoordConv and CoordATT to improve spatial perception for small objects.A lightweight downsampling module MGC(maxpooling-ghost convolution)further reduces model complexity.Experiments on the VisDrone2019 dataset demonstrate that Harmony-YOLO11 improves mAP50 and mAP50-95 by 5.10 and 3.12 percentage points respectively compared to YOLO11n,while reducing parameters and model size by 31%and 25%.Additional evaluations on the TinyPerson dataset confirm the general-ization and robustness of the algorithm.关键词
小目标检测/YOLO11/特征融合/无人机航拍图像Key words
small object detection/YOLO11/feature fusion/UAV aerial images分类
信息技术与安全科学引用本文复制引用
孙中毅,王栋,曹国刚,宗鸣..Harmony-YOLO11:基于高频增强与特征引导的轻量级小目标检测算法[J].计算机工程与应用,2026,62(10):111-122,12.基金项目
国家重点研发计划(2025ZD0547601). （2025ZD0547601）
