Small Object Detection Algorithm for UAV Images Based on Improved YOLOv8

被引：0

作者：

Hou, Ying ^{[1
]}

Wu, Yan ^{[1
]}

Kou, Xurui ^{[1
]}

Huang, Jiachao ^{[1
]}

Tuo, Jindou ^{[1
]}

Wang, Yuqi ^{[1
]}

Huang, Xiaojun ^{[1
]}

机构：

[1] College of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an

来源：

Computer Engineering and Applications | 2025年 / 61卷 / 11期

关键词：

attention mechanism; deep learning; Focaler-CIoU loss function; object detection; unmanned aerial vehicle (UAV); YOLOv8;

D O I：

10.3778/j.issn.1002-8331.2411-0214

中图分类号：

学科分类号：

摘要：

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. © 2025 Journal of Computer Engineering and Applications Beijing Co., Ltd.; Science Press. All rights reserved.

引用

页码：83 / 92

页数：9

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