Harmony-YOLO11:基于高频增强与特征引导的轻量级小目标检测算法OA

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.