|本期目录/Table of Contents|

[1]夏正洪,杨磊,刘璐,等.基于改进YOLOv8n的雨天场景中飞机铆钉检测方法*[J].中国安全生产科学技术,2025,21(1):195-201.[doi:10.11731/j.issn.1673-193x.2025.01.025]
　XIA Zhenghong,YANG Lei,LIU Lu,et al.Detection method of aircraft rivets in rainy scene based on improved YOLOv8n[J].JOURNAL OF SAFETY SCIENCE AND TECHNOLOGY,2025,21(1):195-201.[doi:10.11731/j.issn.1673-193x.2025.01.025]
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基于改进YOLOv8n的雨天场景中飞机铆钉检测方法*
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《中国安全生产科学技术》[ISSN:1673-193X/CN:11-5335/TB]

卷:
21
期数:
2025年1期
页码:
195-201
栏目:
职业安全卫生管理与技术
出版日期:
2025-01-30

文章信息/Info

Title:
Detection method of aircraft rivets in rainy scene based on improved YOLOv8n
文章编号:
1673-193X(2025)-01-0195-07
作者:
夏正洪; 杨磊; 刘璐; 何琥; 钟吉飞
(1.中国民用航空飞行学院 空中交通管理学院，四川 广汉 618307；
2.中国航空工业集团公司成都飞机设计研究所，四川 成都 610091)
Author(s):
XIA Zhenghong;  YANG Lei;  LIU Lu;  HE Hu;  ZHONG Jifei
(1.School of Air Traffic Management,Civil Aviation Flight University of China,Guanghan Sichuan 618307,China;
2.China Aviation Industry Corporation Chengdu Aircraft Design and Research Institute,Chengdu Sichuan 610091,China)
关键词:
航空安全; 小目标检测; 飞机铆钉; 动态蛇形卷积; 可变形注意力机制
Keywords:
aviation safety;  small objection detection;  aircraft rivet;  dynamic snake convolution;  deformable attention mechanism
分类号:
X949;V355.1
DOI:
10.11731/j.issn.1673-193x.2025.01.025
文献标志码:
A
摘要:
为解决雨天场景中飞机表面附着与铆钉大小、形状相似的水滴而导致机务工程师在绕机检查过程中易出现铆钉误检的问题，提出1种基于改进YOLOv8n的飞机铆钉小目标检测方法。首先，改进C2f层，融入动态蛇形卷积，以捕捉复杂多变的全局形态特征；其次，在主干网络中嵌入可变形注意力机制，自适应调整对不同区域的关注度；然后，增加1个160×160的小目标检测层，提高小目标的检测能力；最后，使用斯库拉交并比（SIoU）边界框损失函数，提升模型训练速度和推理准确性，基于自建的飞机铆钉和雨滴数据集进行消融实验和对比实验。研究结果表明：本文所提算法在雨天场景下的铆钉检测精确度、召回率、mAP值分别较YOLOv8n提升7.4，4.0，7.8百分点，较其他主流算法也有显著提升。研究结果可为特殊天气下的飞机铆钉检测提供理论基础。
Abstract:
In order to solve the problem that the aircraft surface is attached with the water droplets with similar size and shape to the rivets in the rainy scene,leading to the misidentification of rivets by maintenance engineers during the inspection process around the aircraft,a small object detection method for aircraft rivets based on the improved YOLOv8n was proposed.Firstly,the C2f layer was improved with incorporating the dynamic snake convolution to capture the complex and diverse global morphological features.Secondly,a deformable attention mechanism was embedded in the backbone network to adaptively adjust the attention to different regions.Then,a 160×160 small object detection layer was added to improve the detection ability of small objects.Finally,the bounding box loss function of SCYLLA Intersection over Union (SIoU) was used to improve the training speed and inference accuracy of the model.Based on the self-built aircraft rivet and raindrop datasets,the ablation experiments and comparative experiments were conducted.The results show that the accuracy,recall,and mAP value of rivet detection in the rainy scene by the algorithm proposed in this paper improves by 7.4,4.0 and 7.8 percentage points respectively compared with the YOLOv8n algorithm,also with significant improvement than other mainstream algorithms.The research results can provide a theoretical foundation for the detection of aircraft rivets under special weather conditions.

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备注/Memo

备注/Memo:
收稿日期： 2024-08-04
* 基金项目： 高校基本科研业务费资助项目（24CAFUC04030,24CAFUC03047）；中国民用航空飞行学院大学生创新创业项目（S202410624237）
作者简介： 夏正洪，硕士，教授，主要研究方向为基于深度学习的航空运行安全风险评价。
更新日期/Last Update: 2025-01-26