Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network

IF 2.2 4区 工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY Cmes-computer Modeling in Engineering & Sciences Pub Date : 2023-01-01 DOI:10.32604/cmes.2023.044709
Wangchen Yan, Jinbao Yang, Xin Luo
{"title":"Quick Weighing of Passing Vehicles Using the Transfer-Learning-Enhanced Convolutional Neural Network","authors":"Wangchen Yan, Jinbao Yang, Xin Luo","doi":"10.32604/cmes.2023.044709","DOIUrl":null,"url":null,"abstract":"Transfer learning could reduce the time and resources required for the training of new models and be therefore important in generalized applications of the trained machine learning algorithms. In this study, a transfer learning-enhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify the axle weight and the gross weight of the typical two-axle, three-axle, and five-axle vehicles as they crossed the bridge with different loading routes and speeds. Then, the pre-trained CNN model was transferred by fine-tuning to weigh the moving vehicles on another bridge. Finally, the identification accuracy and the amount of training data required were compared between the two CNN models. Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%. Moreover, the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model, showing its promising potentials in the actual applications.","PeriodicalId":10451,"journal":{"name":"Cmes-computer Modeling in Engineering & Sciences","volume":"19 1","pages":"0"},"PeriodicalIF":2.2000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cmes-computer Modeling in Engineering & Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.32604/cmes.2023.044709","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Transfer learning could reduce the time and resources required for the training of new models and be therefore important in generalized applications of the trained machine learning algorithms. In this study, a transfer learning-enhanced convolutional neural network (CNN) was proposed to identify the gross weight and the axle weight of moving vehicles on the bridge. The proposed transfer learning-enhanced CNN model was expected to weigh different bridges based on a small amount of training datasets and provide high identification accuracy. First of all, a CNN algorithm for bridge weigh-in-motion (B-WIM) technology was proposed to identify the axle weight and the gross weight of the typical two-axle, three-axle, and five-axle vehicles as they crossed the bridge with different loading routes and speeds. Then, the pre-trained CNN model was transferred by fine-tuning to weigh the moving vehicles on another bridge. Finally, the identification accuracy and the amount of training data required were compared between the two CNN models. Results showed that the pre-trained CNN model using transfer learning for B-WIM technology could be successfully used for the identification of the axle weight and the gross weight for moving vehicles on another bridge while reducing the training data by 63%. Moreover, the recognition accuracy of the pre-trained CNN model using transfer learning was comparable to that of the original model, showing its promising potentials in the actual applications.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于迁移学习增强卷积神经网络的过路车辆快速称重
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Cmes-computer Modeling in Engineering & Sciences
Cmes-computer Modeling in Engineering & Sciences ENGINEERING, MULTIDISCIPLINARY-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
CiteScore
3.80
自引率
16.70%
发文量
298
审稿时长
7.8 months
期刊介绍: This journal publishes original research papers of reasonable permanent value, in the areas of computational mechanics, computational physics, computational chemistry, and computational biology, pertinent to solids, fluids, gases, biomaterials, and other continua. Various length scales (quantum, nano, micro, meso, and macro), and various time scales ( picoseconds to hours) are of interest. Papers which deal with multi-physics problems, as well as those which deal with the interfaces of mechanics, chemistry, and biology, are particularly encouraged. New computational approaches, and more efficient algorithms, which eventually make near-real-time computations possible, are welcome. Original papers dealing with new methods such as meshless methods, and mesh-reduction methods are sought.
期刊最新文献
ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules. A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition ER-Net: Efficient Recalibration Network for Multi-View Multi-Person 3D Pose Estimation Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine Introduction to the Special Issue on Computational Intelligent Systems for Solving Complex Engineering Problems: Principles and Applications
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1