舰船电子工程2025，Vol.45Issue(3)：119-125,7.DOI:10.3969/j.issn.1672-9730.2025.03.025
改进YOLOv8算法的机场外来物检测研究
Research on Airport Foreign Object Detection Based on Improved YOLOv8 Algorithm
摘要
Abstract
In order to solve the problems of poor detection stability and missed detection in the airport foreign object detection method,this paper improves the YOLOv8 algorithm.Firstly,dynamic convolution(ODConv)is used by introducing a learnable de-formation module that dynamically adjusts the shape,size,and channel dimension of the convolution kernel.This optimization of the convolution process focuses on the shape,size,and scale changes of airport foreign objects,achieving efficient extraction of im-age feature information.Secondly,the C2f_DAConv module is designed to reduce the number of parameters in the algorithm.Then,based on the PANet network architecture,the P2 feature layer of the backbone network is fused,and the PANet network architec-ture is changed to BiFPN.The network realizes the efficient fusion of low-level detail feature information and high-level semantic feature information,reducing information loss of foreign object target features.Finally,to solve the positioning error problem be-tween the prediction box and the target box,the loss function is changed to Inner SIoU,optimizing the calculation process of the al-gorithm,accelerating the convergence speed of algorithm training,and improving the detection accuracy of the algorithm.The exper-imental results show that the improved algorithm has 35.5%fewer parameters than the original YOLOv8 algorithm.The mean aver-age precision(mAP)reaches 97.3%,an increase of 2.0%,and the recall rate(Recall)is 95.5%,an increase of 5.2%.Compara-tive analysis of the F1 curve,P-R curve,and Recall curve shows that the improved algorithm significantly improves detection stabil-ity and effectively solves the problem of missed detection of foreign objects at airports.关键词
改进YOLOv8算法/FOD检测/动态卷积/机场安全Key words
improved YOLOv8 algorithm/FOD detection/dynamic convolution/airport safety分类
资源环境引用本文复制引用
郭九霞,李金润,王义龙,李静远,唐锐..改进YOLOv8算法的机场外来物检测研究[J].舰船电子工程,2025,45(3):119-125,7.基金项目
国家自然科学基金项目(编号:72201268) （编号:72201268）
四川省自然科学基金项目(编号:U2333207) （编号:U2333207）
四川省社会科学基金项目(编号:SCJJ23ND186) （编号:SCJJ23ND186）
中央高校基本科研业务费专项资金(编号:PHD2023-041) （编号:PHD2023-041）
民航教育人才类项目(编号:MHJY2023010) （编号:MHJY2023010）
中央高校教育教学改革专项资金(编号:E2024024)资助. （编号:E2024024）
