{"title":"基于深度学习的路面状况智能检测","authors":"Lele Zheng , Jingjing Xiao , Yinghui Wang , Wangjie Wu , Zhirong Chen , Dongdong Yuan , Wei Jiang","doi":"10.1016/j.autcon.2024.105772","DOIUrl":null,"url":null,"abstract":"<div><p>The intelligent detection of pavement distress using deep learning methods has consistently been a hotspot in pavement maintenance. This paper aims to offer new insights to promote research and application in this field through bibliometric analysis. Utilizing publications from the Web of Science Core Collection spanning from 2016 to 2024 as the database, this paper conducts a systematic analysis of statistical data concerning the annual publication numbers, countries/regions, institutions, authors, hot papers, disciplines, and journals. Based on deep learning models, datasets, and the state of practice, this analysis explores the hotspots and fronts of this field. It identifies gaps, challenges, and future research directions, including the exploration and optimization of models, the quality and variability of datasets, the evolution of data acquisition methods, the impact of the state of practice, the prospects of unmanned detection technologies, the integration of multi-source heterogeneous data, and the potential of digital twin technologies.</p></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":null,"pages":null},"PeriodicalIF":9.6000,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep learning-based intelligent detection of pavement distress\",\"authors\":\"Lele Zheng , Jingjing Xiao , Yinghui Wang , Wangjie Wu , Zhirong Chen , Dongdong Yuan , Wei Jiang\",\"doi\":\"10.1016/j.autcon.2024.105772\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The intelligent detection of pavement distress using deep learning methods has consistently been a hotspot in pavement maintenance. This paper aims to offer new insights to promote research and application in this field through bibliometric analysis. Utilizing publications from the Web of Science Core Collection spanning from 2016 to 2024 as the database, this paper conducts a systematic analysis of statistical data concerning the annual publication numbers, countries/regions, institutions, authors, hot papers, disciplines, and journals. Based on deep learning models, datasets, and the state of practice, this analysis explores the hotspots and fronts of this field. It identifies gaps, challenges, and future research directions, including the exploration and optimization of models, the quality and variability of datasets, the evolution of data acquisition methods, the impact of the state of practice, the prospects of unmanned detection technologies, the integration of multi-source heterogeneous data, and the potential of digital twin technologies.</p></div>\",\"PeriodicalId\":8660,\"journal\":{\"name\":\"Automation in Construction\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-09-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Automation in Construction\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0926580524005089\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CONSTRUCTION & BUILDING TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580524005089","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
利用深度学习方法对路面病害进行智能检测一直是路面养护领域的热点。本文旨在通过文献计量分析,为推动该领域的研究与应用提供新的见解。本文以 Web of Science 核心文库中 2016 年至 2024 年的出版物为数据库,对年度出版物数量、国家/地区、机构、作者、热点论文、学科和期刊等统计数据进行了系统分析。基于深度学习模型、数据集和实践状况,本分析探讨了该领域的热点和前沿问题。它指出了差距、挑战和未来研究方向,包括模型的探索和优化、数据集的质量和可变性、数据采集方法的演变、实践状况的影响、无人探测技术的前景、多源异构数据的整合以及数字孪生技术的潜力。
Deep learning-based intelligent detection of pavement distress
The intelligent detection of pavement distress using deep learning methods has consistently been a hotspot in pavement maintenance. This paper aims to offer new insights to promote research and application in this field through bibliometric analysis. Utilizing publications from the Web of Science Core Collection spanning from 2016 to 2024 as the database, this paper conducts a systematic analysis of statistical data concerning the annual publication numbers, countries/regions, institutions, authors, hot papers, disciplines, and journals. Based on deep learning models, datasets, and the state of practice, this analysis explores the hotspots and fronts of this field. It identifies gaps, challenges, and future research directions, including the exploration and optimization of models, the quality and variability of datasets, the evolution of data acquisition methods, the impact of the state of practice, the prospects of unmanned detection technologies, the integration of multi-source heterogeneous data, and the potential of digital twin technologies.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.