{"title":"计算机视觉与机器学习技术在路面缺陷与损伤自动检测中的研究进展","authors":"Xuejing Chen, Sira Yongchareon, Martin Knoche","doi":"10.3233/scs-230001","DOIUrl":null,"url":null,"abstract":"As the pace grows in the development of image processing techniques and the current applications rise in machine learning and deep learning techniques for visual inspections and physical assessment, this article reviews the existing literature. It provides a detailed synthesis of the overview of surface pavement conditions, computer-vision-based technologies for road damage detection, various datasets and data collection methods. We analyse and compare different machine-learning methods and models proposed in the literature and identify challenges that need to be addressed in the future in road surface defect detection.","PeriodicalId":299673,"journal":{"name":"J. Smart Cities Soc.","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A review on computer vision and machine learning techniques for automated road surface defect and distress detection\",\"authors\":\"Xuejing Chen, Sira Yongchareon, Martin Knoche\",\"doi\":\"10.3233/scs-230001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the pace grows in the development of image processing techniques and the current applications rise in machine learning and deep learning techniques for visual inspections and physical assessment, this article reviews the existing literature. It provides a detailed synthesis of the overview of surface pavement conditions, computer-vision-based technologies for road damage detection, various datasets and data collection methods. We analyse and compare different machine-learning methods and models proposed in the literature and identify challenges that need to be addressed in the future in road surface defect detection.\",\"PeriodicalId\":299673,\"journal\":{\"name\":\"J. Smart Cities Soc.\",\"volume\":\"46 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Smart Cities Soc.\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3233/scs-230001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Smart Cities Soc.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/scs-230001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A review on computer vision and machine learning techniques for automated road surface defect and distress detection
As the pace grows in the development of image processing techniques and the current applications rise in machine learning and deep learning techniques for visual inspections and physical assessment, this article reviews the existing literature. It provides a detailed synthesis of the overview of surface pavement conditions, computer-vision-based technologies for road damage detection, various datasets and data collection methods. We analyse and compare different machine-learning methods and models proposed in the literature and identify challenges that need to be addressed in the future in road surface defect detection.