{"title":"Bridge defect detection using small sample data with deep learning and Hyperspectral imaging","authors":"Xiong Peng, Pengtao Wang, Kun Zhou, Zhipeng Yan, Xingu Zhong, Chao Zhao","doi":"10.1016/j.autcon.2024.105900","DOIUrl":null,"url":null,"abstract":"The visual sensing method is an effective way to address long-term health monitoring of bridges. However, bridge defect detection based on visible light imaging mainly relies on grayscale and regional edge gradient information, which brings challenges such as limited information dimensions and complex background. This paper introduces a bridge defect detection method that leverages hyperspectral imaging, utilizing the unique integration of spectral and spatial information. Also a convolutional neural network algorithm with dual branches and dense blocks for spectral feature extraction is developed. This framework includes spectral and spatial branches, which independently extract respective features in order to minimize mutual interference. Compared with the support vector machine and traditional deep learning algorithms, the proposed method attains an overall model prediction accuracy(OA) of 98.57 %, an average accuracy (AA) of 98.16 %, and a Kappa coefficient of 0.9814, representing the best classification performance on small sample datasets.","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"20 1","pages":""},"PeriodicalIF":9.6000,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.autcon.2024.105900","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
Abstract
The visual sensing method is an effective way to address long-term health monitoring of bridges. However, bridge defect detection based on visible light imaging mainly relies on grayscale and regional edge gradient information, which brings challenges such as limited information dimensions and complex background. This paper introduces a bridge defect detection method that leverages hyperspectral imaging, utilizing the unique integration of spectral and spatial information. Also a convolutional neural network algorithm with dual branches and dense blocks for spectral feature extraction is developed. This framework includes spectral and spatial branches, which independently extract respective features in order to minimize mutual interference. Compared with the support vector machine and traditional deep learning algorithms, the proposed method attains an overall model prediction accuracy(OA) of 98.57 %, an average accuracy (AA) of 98.16 %, and a Kappa coefficient of 0.9814, representing the best classification performance on small sample datasets.
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.