Name
Title | ||
|---|---|---|
Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22072722 | SENSORS |
Keywords | DocType | Volume |
survey, computer vision, defect inspection, condition assessment, sewer pipes | Journal | 22 |
Issue | ISSN | Citations |
7 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanfen Li | 1 | 1 | 2.40 |
| Hanxiang Wang | 2 | 4 | 1.75 |
| L Minh Dang | 3 | 0 | 0.34 |
| Hyoung-Kyu Song | 4 | 0 | 0.68 |
| Hyeonjoon Moon | 5 | 1886 | 267.81 |