Abstract
Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference131 articles.
1. The 2019 Canadian Infrastructure Report Card (CIRC)http://canadianinfrastructure.ca/downloads/canadian-infrastructure-report-card-2019.pdf
2. Sewer asset management – state of the art and research needs
3. 2021 Report Card for America’s Infrastructure 2021 Wastewaterhttps://infrastructurereportcard.org/wp-content/uploads/2020/12/Wastewater-2021.pdf
4. Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
5. State of the art in sensor technologies for sewer inspection
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