北京航空航天大学学报2026，Vol.52Issue(1)：38-48,11.DOI:10.13700/j.bh.1001-5965.2023.0744
基于改进YOLOv8的飞机蒙皮缺陷检测算法
Aircraft skin defect detection algorithm based on enhanced YOLOv8
摘要
Abstract
In order to solve the problem that traditional aircraft skin defect detection relies on human eye observation,which leads to reduced efficiency due to easy fatigue of the human eye and limited individual cognition,an aircraft skin defect detection algorithm based on improved YOLOv8 is proposed.Improve the data improvement strategy and propose a new one that combines slice reasoning with mosaic.Integrate the residual block into the feature extraction network to enhance the network expression ability and improve the accuracy of the model in aircraft skin defect detection tasks.Use the triplet attention module to strengthen the feature fusion network and lower the false and missed detection rates of small target samples.Optimize the structure of the detection head so that the network can better effectively combine shallow information with depth information.On the aircraft skin defect data set,experimental results indicate that the revised algorithm's mean average precision(mAP)and recall rate have increased by 3.6%and 3.7%,respectively,in comparison to the most recent YOLOv8 algorithm.The mAP and recall rate on the public data set VOC2007 increased by 2.9%and 2.2%,respectively.关键词
YOLOv8算法/表面缺陷检测/数据增强/目标检测/注意力机制Key words
YOLOv8 algorithm/surface defect detection/data augmentation/object detection/attention mechanism分类
航空航天引用本文复制引用
章东平,王杼涛,夏岳键,徐云超,林丽莉..基于改进YOLOv8的飞机蒙皮缺陷检测算法[J].北京航空航天大学学报,2026,52(1):38-48,11.基金项目
浙江省重点研发计划(2022C01005,2023C01032,2023C01030) Zhejiang Key R&D Project of China(2022C01005,2023C01032,2023C01030) （2022C01005,2023C01032,2023C01030）
