计算机工程与应用2025，Vol.61Issue(11)：83-92,10.DOI:10.3778/j.issn.1002-8331.2411-0214
改进YOLOv8的无人机航拍图像小目标检测算法
Small Object Detection Algorithm for UAV Images Based on Improved YOLOv8
摘要
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 back-bone network,which effectively enhances the ability to capture shallow detail feature information of small objects.Subse-quently,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 tar-get size adaptive penalty factor is adopted in the Head network to optimize the regression positioning loss.On the Vis-Drone-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.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Key words
unmanned aerial vehicle(UAV)/object detection/deep learning/YOLOv8/attention mechanism/Focaler-CIoU loss function分类
信息技术与安全科学引用本文复制引用
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):83-92,10.基金项目
国家自然科学基金(62401459) （62401459）
国家大学生创新训练计划项目(202410704059) （202410704059）
西安科技大学创新创业教育教学研究专项(2010624018). （2010624018）
