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| YOLOv8s based lightweight algorithm for small object detection in aerial imagery |
Kaijun WU1,2( ),Yunqi ZHENG1,Ding WEI2,Haixiang YUAN1 |
1. School of Mathematical and Physics, Lanzhou Jiaotong University, Lanzhou 730070, China
2. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China |
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Abstract An improved lightweight small object detection method based on YOLOv8s was proposed to address the challenges of complex background interference, small object sizes, and limited device resources in unmanned aerial vehicle (UAV) image object detection. A high-resolution detection head was reconstructed to improve the sensitivity to tiny objects. A dynamic multi-scale aggregation network was designed to adaptively adjust the receptive field for scale-variant targets, while an efficient multi-scale feature deep fusion module was proposed to integrate features from different semantic levels. An efficient channel-spatial attention module was introduced to further refine fused features. Experiments on the VisDrone2021 dataset demonstrated that the proposed method achieved improvements of over 5.0% in average precision, precision at different intersection over union thresholds, and detection precision for small and medium-sized objects, with a 16.25% reduction in parameters. Tests on the DOTA dataset validated the generalization ability of the proposed model. Results demonstrate that the proposed method improves the detection performance while achieving the model light weighting, indicating its practical value for object detection in UAV aerial images.
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Received: 20 July 2025
Published: 20 July 2026
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| Fund: 甘肃省重点研发计划资助项目（25YFGA047）；鄂尔多斯市重点研发计划资助项目（YF20250245）. |
基于YOLOv8s的轻量化航拍图像小目标检测算法
为了解决无人机（UAV）航拍图像目标检测面临的复杂背景干扰、目标尺寸小以及设备资源有限等挑战，提出基于YOLOv8s的轻量化小目标检测方法. 为了增强微小目标检测能力，重建高分辨率检测头. 设计动态多尺度聚合网络，动态调整感受野以适应目标尺度变化. 提出高效多尺度特征深度融合模块，自适应整合无人机图像不同层级提取的特征信息. 将融合后的特征进行双重精炼，建立高效通道-空间注意力模块. 在VisDrone2021公开数据集上的实验结果显示，改进后算法在验证集上的平均精度、不同交并比阈值下精度以及中小尺度目标检测精度均提升5.0%以上. 在保持较低计算量的同时，参数量减少16.25%. 在DOTA数据集上验证模型的泛化性，所提算法在提高检测性能的同时，实现了模型轻量化，可以应用于无人机航拍图像目标检测.
关键词：
目标检测,
航拍图像,
YOLOv8s,
特征融合,
特征提取
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