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| Improved YOLOv8s lightweight small target detection algorithm of UAV aerial image |
Yahong ZHAI( ),Yaling CHEN,Longyan XU*(),Yu GONG |
| School of Electrical and Information Engineering, Hubei University of Automotive Technology, Shiyan 442002, China |
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Abstract A lightweight small target detection algorithm called RTA-YOLOv8s was proposed in order to address the challenges of complex backgrounds, small target, and limited device resources in UAV images. The RepVGG module was introduced into the backbone network to enhance feature extraction capabilities. A tri-branch attention mechanism was applied to reduce false positive and false negative rates. A dedicated small target detection head was integrated to improve detection accuracy. The WIoUv3 loss function was adopted to improve localization and robustness. The experimental results showed that the RTA-YOLOv8s algorithm achieved a mAP50 of 44.9% and detection speed of 88.5 frame per second on the VisDrone dataset. mAP50 increased by 6.1%, detection accuracy increased by 4.7%, and params reduced by 13.9% compared with YOLOv8s. The improved algorithm effectively addresses the poor detection performance in complex UAV scenes, and balances accuracy and speed. The user-friendly interface design enables result visualization, making detection tasks more intuitive and easier to operate, and is suitable for UAV target detection.
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Received: 16 July 2024
Published: 28 July 2025
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| Fund: 湖北省教育厅科研计划资助项目(D202111802)；湖北省科技厅重点研发计划资助项目(2022BEC008)；中南民族大学信息物理融合智能计算国家民委重点实验室开放基金资助项目(CPFIC202402). |
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Corresponding Authors:
Longyan XU
E-mail: zhaiyh_dy@huat.edu.cn;xuly_dy@huat.edu.cn
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改进YOLOv8s的轻量级无人机航拍小目标检测算法
针对无人机航拍图像背景复杂、目标尺寸小及设备资源有限等问题，提出轻量化小目标检测算法RTA-YOLOv8s. 在主干网络引入RepVGG模块，增强特征提取能力. 应用三分支注意力机制，降低小目标的误检率和漏检率. 增加小目标专用检测头，提高对小目标的检测能力. 采用WIoUv3作为损失函数，提升模型的定位性能和鲁棒性. 实验结果表明，RTA-YOLOv8s算法在VisDrone数据集中的mAP50达到44.9%，检测速度达到88.5帧/s. 与基线算法YOLOv8s相比，mAP50提升了6.1%，检测准确率提高了4.7%，参数量减少了13.9%. 利用改进的算法，有效解决了复杂场景下检测效果不佳的问题，在精度和速度之间取得了很好的平衡. 设计人机界面，实现结果可视化，使检测任务更加直观且易操作，适合无人机航拍的目标检测.
关键词：
无人机(UAV),
小目标检测,
YOLOv8s,
轻量化方法,
注意力机制
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