高技术通讯2024，Vol.34Issue(7)：765-775,11.DOI:10.3772/j.issn.1002-0470.2024.07.010
基于改进YOLOv8的无人机图像小目标检测算法
Small object detection algorithm for unmanned aerial vehicle image based on improved YOLOv8
摘要
Abstract
Aiming at the problem of low detection accuracy caused by small object size and little feature information in unmanned aerial vehicle images,an improved small object detection algorithm of YOLOv8n is proposed.Firstly,the Wise-IoU loss function is introduced,which enhances the network's focus on ordinary quality anchor frames through a dynamic non-monotonic focusing mechanism,improving the generalization ability of the algorithm.Sec-ondly,in order to improve the accuracy of small object detection,a new feature fusion structure,the Bi-SODL structure,is constructed by adding a small object detection layer(SODL)and bidirectional feature pyramid net-work(BiFPN).SODL enables the network to capture the shallow feature information of the small object more ade-quately.BiFPN can achieve the information exchange and fusion between feature layers of different scales.Finally,LSKBlock attention mechanism is introduced,which processes the input features through spatial selection mecha-nism and weighting,further improving the performance and robustness of small object detection.The experimental results show that the detection accuracy metrics P,mAP_0.50 and mAP_0.50∶0.95 on the VisDrone2019 data-set are increased by 6.4％,8.3％and 5.2％respectively,and the number of parameters is reduced by 25.78％.The improved measures make the detection performance better than many mainstream algorithms,which proves the effectiveness of the improved algorithm.关键词
小目标检测/YOLOv8n/特征融合/注意力机制Key words
small object detection/YOLOv8n/feature fusion/attention mechanism引用本文复制引用
李云文,冯宇平,安文志,赵军,聂国潇..基于改进YOLOv8的无人机图像小目标检测算法[J].高技术通讯,2024,34(7):765-775,11.基金项目
国家自然科学基金(61971253)和国家级大学生创新创业训练项目(202310426296,202310426356)资助. （61971253）
