现代电子技术2025，Vol.48Issue(5)：68-74,7.DOI:10.16652/j.issn.1004-373x.2025.05.011
WS-YOLO:航拍视角小目标检测方法
WS-YOLO:Small object detection method for aerial photography viewpoint
摘要
Abstract
In the aerial photography small object detection,due to the complexity of the image background,the object scale is small,while the spatial scale varies greatly,which results in a series of problems such as leakage and false detection in UAV aerial photography.For this reason,an improved small object detection method WS-YOLO for UAV aerial photography viewpoint is proposed based on YOLOv8n,so as to improve detection accuracy and real-time performance,and realize the lightweight of the UAV aerial photography.Firstly,the YOLOv8n network structure is reconstructed and 160×160 feature maps corresponding to the prediction head are added,so as to improve the accuracy and robustness of the model for small-scale object detection.Then,the SPD is embedded after the convolutional layer of the backbone network,so as to prevent the loss of fine-grained information and learn less effective feature representations,so that its ability to recognize low-resolution images is improved.Finally,the CIoU loss function is replaced with the WIoU(v3)loss function,so as to mitigate the effect of low image quality on the process of detection.The final experimental results show that in the same environment and with the same parameters,the accuracy of the proposed WS-YOLO is improved by 8.9% and 7.7% on the VisDrone2019 dataset and the AI-TOD dataset,respectively,in comparison with that of the original algorithm.In addition,its parameters are reduced and its FPS is within a reasonable range.It is verified that the proposed WS-YOLO can improve the effectiveness in small object detection for the UAV aerial photography viewpoint.关键词
无人机航拍/轻量化/YOLOv8n/SPD/WIoU/VisDrone2019/AI-TODKey words
UAV aerial photograph/lightweight/YOLOv8n/SPD/WIoU/VisDrone2019/AI-TOD分类
电子信息工程引用本文复制引用
王峣,蒋行国,秦海洋,黎明,刁豪杰..WS-YOLO:航拍视角小目标检测方法[J].现代电子技术,2025,48(5):68-74,7.基金项目
四川轻化工大学人才引进项目(2019RC12) （2019RC12）
