Modified Crack Detection of Sewer Conduit with Low-Resolution Images
| Autor: | Taejun Cho, Byung Jik Son |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Computer science
Threshold limit value 0211 other engineering and technologies low resolution image Image processing 02 engineering and technology lcsh:Technology lcsh:Chemistry Electrical conduit 021105 building & construction 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes Pixel business.industry lcsh:T Process Chemistry and Technology Low resolution crack detection General Engineering lcsh:QC1-999 Computer Science Applications image processing lcsh:Biology (General) lcsh:QD1-999 user algorithm lcsh:TA1-2040 020201 artificial intelligence & image processing Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 11, Iss 2263, p 2263 (2021) Applied Sciences Volume 11 Issue 5 |
| ISSN: | 2076-3417 |
| Popis: | Imaging devices of less than 300,000 pixels are mostly used for sewage conduit exploration due to the petty nature of the survey industry in Korea. Particularly, devices of less than 100,000 pixels are still widely used, and the environment for image processing is very dim. Since the sewage conduit images covered in this study have a very low resolution (240 × 320 = 76,800 pixels), it is very difficult to detect cracks. Because most of the resolutions of the sewer conduit images are very low in Korea, this problem of low resolution was selected as the subject of this study. Cracks were detected through a total of six steps of improving the crack in Step 2, finding the optimal threshold value in Step 3, and applying an algorithm to detect cracks in Step 5. Cracks were effectively detected by the optimal parameters in Steps 2 and 3 and the user algorithm in Step 5. Despite the very low resolution, the cracked images showed a 96.4% accuracy of detection, and the non-cracked images showed 94.5% accuracy. Moreover, the analysis was excellent in quality. It is believed that the findings of this study can be effectively used for crack detection with low-resolution images. |
| Databáze: | OpenAIRE |
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