改进YOLOv8的无人机航拍图像小目标检测算法

doi:10.3778/j.issn.1002-8331.2411-0214

摘要/Abstract

摘要： 无人机拍摄影像存在大量分布密集的小目标，针对通用目标检测方法对小目标容易造成漏检和错检的问题，提出了一种改进YOLOv8的无人机航拍图像小目标检测算法。利用高分辨率浅层特征信息具有较小的感受野和更精细的空间信息特性，改进算法增加小目标物体检测头，采用四个特征检测头提升小目标检测率。设计构造ConvSPD卷积模块和BiFormer注意力增强模块的小目标检测模块组改进YOLOv8骨干网络，有效增强小目标浅层细节特征信息的捕获能力。为确保模型的硬件终端部署需求，采用可重参数化的Rep-PAN模型优化Neck网络。Head网络采用Focaler-CIoU损失函数优化回归定位损失，提高定位精度。在VisDrone-2019数据集上，改进算法平均检测精度达到51.2%，比YOLOv8提高10.9个百分点，检测速度为63.7 FPS，具有良好的实时性。

关键词: 无人机（UAV）, 目标检测, 深度学习, YOLOv8算法, 注意力机制, Focaler-CIoU损失函数

Abstract: Unmanned aerial vehicle (UAV) images have a large number of densely distributed small targets, which easily cause the problems of small target missed detection and false detection. Therefore, an improved YOLOv8 small target detection algorithm for UAV images is proposed. Firstly, by utilizing high-resolution shallow feature information with smaller receptive fields and finer spatial information features, a small object detection head is added and four feature extraction heads are used to improve the small object detection rate. Secondly, a small object detection module group with ConvSPD convolution module and BiFormer attention enhancement module is designed to improve the YOLOv8 backbone network, which effectively enhances the ability to capture shallow detail feature information of small objects. Subsequently, to meet the hardware deployment requirements of the model, a reparameterizable Rep-PAN model is adopted to optimize the Neck network. Finally, in order to improve the positioning accuracy, the Focaler-CIoU loss function with target size adaptive penalty factor is adopted in the Head network to optimize the regression positioning loss. On the VisDrone-2019 dataset, the improved algorithm obtains 51.2% average detection accuracy and is 10.9 percentage point higher than YOLOv8. In addition, its detection frame rate achieves 63.7 FPS, and it has good real-time performance.

Key words: unmanned aerial vehicle (UAV), object detection, deep learning, YOLOv8, attention mechanism, Focaler-CIoU loss function

侯颖, 吴琰, 寇旭瑞, 黄嘉超, 庹金豆, 王裕旗, 黄晓俊. 改进YOLOv8的无人机航拍图像小目标检测算法[J]. 计算机工程与应用, 2025, 61(11): 83-92.

HOU Ying, WU Yan, KOU Xurui, HUANG Jiachao, TUO Jindou, WANG Yuqi, HUANG Xiaojun. Small Object Detection Algorithm for UAV Images Based on Improved YOLOv8[J]. Computer Engineering and Applications, 2025, 61(11): 83-92.

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