• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于深度神经网络的飞机产品铆钉连接缺陷识别。

Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products.

机构信息

Laboratory of Intellectual Control Systems and Modeling, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 117997 Moscow, Russia.

Laboratory of Cyber-Physical Systems, V.A. Trapeznikov Institute of Control Sciences of Russian Academy of Sciences, 117997 Moscow, Russia.

出版信息

Sensors (Basel). 2022 Apr 29;22(9):3417. doi: 10.3390/s22093417.

DOI:10.3390/s22093417
PMID:35591107
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9105654/
Abstract

The mathematical statement of the problem of recognizing rivet joint defects in aircraft products is given. A computational method for the recognition of rivet joint defects in aircraft equipment based on video images of aircraft joints has been proposed with the use of neural networks YOLO-V5 for detecting and MobileNet V3 Large for classifying rivet joint states. A novel dataset based on a real physical model of rivet joints has been created for machine learning. The accuracy of the result obtained during modeling was 100% in both binary and multiclass classification.

摘要

给出了飞机产品中铆钉连接缺陷识别问题的数学表述。提出了一种基于飞机接头视频图像的飞机设备铆钉连接缺陷识别计算方法，该方法使用 YOLO-V5 神经网络进行检测，MobileNet V3 Large 进行铆钉连接状态分类。为机器学习创建了一个基于真实物理模型的新型铆钉数据集。在建模过程中获得的结果的准确率在二进制和多类分类中均达到了 100%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/8fc054799e9f/sensors-22-03417-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/7891d3655d40/sensors-22-03417-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/0c4e7e9527f1/sensors-22-03417-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/0fdb9c4e6732/sensors-22-03417-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/d99c9b06fd0e/sensors-22-03417-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/f66a76362eb4/sensors-22-03417-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/324cd4c54ac1/sensors-22-03417-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/b5f56f2a5a47/sensors-22-03417-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/f3acdf3526ee/sensors-22-03417-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/3a178c2158e6/sensors-22-03417-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/6455f9458afb/sensors-22-03417-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/8fc054799e9f/sensors-22-03417-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/7891d3655d40/sensors-22-03417-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/0c4e7e9527f1/sensors-22-03417-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/0fdb9c4e6732/sensors-22-03417-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/d99c9b06fd0e/sensors-22-03417-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/f66a76362eb4/sensors-22-03417-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/324cd4c54ac1/sensors-22-03417-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/b5f56f2a5a47/sensors-22-03417-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/f3acdf3526ee/sensors-22-03417-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/3a178c2158e6/sensors-22-03417-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/6455f9458afb/sensors-22-03417-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b42b/9105654/8fc054799e9f/sensors-22-03417-g011.jpg

相似文献

1
Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products.基于深度神经网络的飞机产品铆钉连接缺陷识别。
Sensors (Basel). 2022 Apr 29;22(9):3417. doi: 10.3390/s22093417.
2
Defects Recognition Algorithm Development from Visual UAV Inspections.基于视觉无人机检测的缺陷识别算法开发。
Sensors (Basel). 2022 Jun 21;22(13):4682. doi: 10.3390/s22134682.
3
Improving Animal Monitoring Using Small Unmanned Aircraft Systems (sUAS) and Deep Learning Networks.利用小型无人机系统 (sUAS) 和深度学习网络加强动物监测。
Sensors (Basel). 2021 Aug 24;21(17):5697. doi: 10.3390/s21175697.
4
A Particleboard Surface Defect Detection Method Research Based on the Deep Learning Algorithm.基于深度学习算法的刨花板表面缺陷检测方法研究
Sensors (Basel). 2022 Oct 12;22(20):7733. doi: 10.3390/s22207733.
5
Aircraft Image Recognition Network Based on Hybrid Attention Mechanism.基于混合注意力机制的飞机图像识别网络
Comput Intell Neurosci. 2022 Apr 18;2022:4189500. doi: 10.1155/2022/4189500. eCollection 2022.
6
Efficient Violence Detection in Surveillance.高效监控中的暴力检测。
Sensors (Basel). 2022 Mar 13;22(6):2216. doi: 10.3390/s22062216.
7
Comparison of Deep-Learning and Conventional Machine-Learning Methods for the Automatic Recognition of the Hepatocellular Carcinoma Areas from Ultrasound Images.深度学习与传统机器学习方法在自动识别超声图像肝癌区域的比较。
Sensors (Basel). 2020 May 29;20(11):3085. doi: 10.3390/s20113085.
8
Food Image Recognition and Food Safety Detection Method Based on Deep Learning.基于深度学习的食品图像识别与食品安全检测方法。
Comput Intell Neurosci. 2021 Dec 16;2021:1268453. doi: 10.1155/2021/1268453. eCollection 2021.
9
Research on aircraft skin rivet detection technology based on the normal vector-density clustering algorithm.基于法向量-密度聚类算法的飞机蒙皮铆钉检测技术研究
Rev Sci Instrum. 2024 Mar 1;95(3). doi: 10.1063/5.0189065.
10
YOLO Based Breast Masses Detection and Classification in Full-Field Digital Mammograms.基于YOLO的全视野数字乳腺钼靶片中乳腺肿块检测与分类
Comput Methods Programs Biomed. 2021 Mar;200:105823. doi: 10.1016/j.cmpb.2020.105823. Epub 2020 Nov 4.

引用本文的文献

1
Artificial Intelligence-Based Smart Quality Inspection for Manufacturing.基于人工智能的制造业智能质量检测
Micromachines (Basel). 2023 Feb 27;14(3):570. doi: 10.3390/mi14030570.
2
Inspection of Underwater Hull Surface Condition Using the Soft Voting Ensemble of the Transfer-Learned Models.使用迁移学习模型的软投票集成来检查水下船体表面状况。
Sensors (Basel). 2022 Jun 10;22(12):4392. doi: 10.3390/s22124392.
3
Enhancing Precision with an Ensemble Generative Adversarial Network for Steel Surface Defect Detectors (EnsGAN-SDD).

本文引用的文献

1
Comparing YOLOv3, YOLOv4 and YOLOv5 for Autonomous Landing Spot Detection in Faulty UAVs.比较 YOLOv3、YOLOv4 和 YOLOv5 在无人机故障自主着陆点检测中的应用。
Sensors (Basel). 2022 Jan 8;22(2):464. doi: 10.3390/s22020464.
2
High-Accuracy 3-D Sensor for Rivet Inspection Using Fringe Projection Profilometry with Texture Constraint.基于纹理约束的条纹投影轮廓术的高精度三维铆钉检测传感器
Sensors (Basel). 2020 Dec 18;20(24):7270. doi: 10.3390/s20247270.
3
Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network.
基于集成生成对抗网络的钢表面缺陷检测模型增强精度研究（EnsGAN-SDD）。
Sensors (Basel). 2022 Jun 2;22(11):4257. doi: 10.3390/s22114257.
深层金属表面缺陷检测：新基准与检测网络
Sensors (Basel). 2020 Mar 11;20(6):1562. doi: 10.3390/s20061562.