GPU-enabled pavement distress image classification in real time
Pavement assessment is a crucial process for the maintenance of municipal roads. However, the detection of pavement distress is usually performed either manually or offline, which is not only time-consuming and subjective, but also results in an enormous amount of data being stored persistently befo...
| Main Authors: | , , |
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| Format: | Article |
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American Society of Civil Engineers
2016
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| Online Access: | https://eprints.nottingham.ac.uk/35192/ |
| _version_ | 1848795024866672640 |
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| author | Doycheva, Kristina Koch, Christian König, Markus |
| author_facet | Doycheva, Kristina Koch, Christian König, Markus |
| author_sort | Doycheva, Kristina |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Pavement assessment is a crucial process for the maintenance of municipal roads. However, the detection of pavement distress is usually performed either manually or offline, which is not only time-consuming and subjective, but also results in an enormous amount of data being stored persistently before processing. State-of-the-art pavement image processing methods executed on a CPU are not able to analyse pavement images in real time. To compensate this limitation of the methods, we propose an automated approach for pavement distress detection. In particular, GPU implementations of a noise removal, a background correction and a pavement distress detection method were developed. The median filter and the top-hat transform are used to remove noise and shadows in the images. The wavelet transform is applied in order to calculate a descriptor value for classification purposes. The approach was tested on 1549 images. The results show that real-time pre-processing and analysis are possible. |
| first_indexed | 2025-11-14T19:25:31Z |
| format | Article |
| id | nottingham-35192 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:25:31Z |
| publishDate | 2016 |
| publisher | American Society of Civil Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-351922020-05-04T18:00:35Z https://eprints.nottingham.ac.uk/35192/ GPU-enabled pavement distress image classification in real time Doycheva, Kristina Koch, Christian König, Markus Pavement assessment is a crucial process for the maintenance of municipal roads. However, the detection of pavement distress is usually performed either manually or offline, which is not only time-consuming and subjective, but also results in an enormous amount of data being stored persistently before processing. State-of-the-art pavement image processing methods executed on a CPU are not able to analyse pavement images in real time. To compensate this limitation of the methods, we propose an automated approach for pavement distress detection. In particular, GPU implementations of a noise removal, a background correction and a pavement distress detection method were developed. The median filter and the top-hat transform are used to remove noise and shadows in the images. The wavelet transform is applied in order to calculate a descriptor value for classification purposes. The approach was tested on 1549 images. The results show that real-time pre-processing and analysis are possible. American Society of Civil Engineers 2016-07-19 Article PeerReviewed Doycheva, Kristina, Koch, Christian and König, Markus (2016) GPU-enabled pavement distress image classification in real time. Journal of Computing in Civil Engineering . ISSN 1943-5487 (In Press) |
| spellingShingle | Doycheva, Kristina Koch, Christian König, Markus GPU-enabled pavement distress image classification in real time |
| title | GPU-enabled pavement distress image classification in real time |
| title_full | GPU-enabled pavement distress image classification in real time |
| title_fullStr | GPU-enabled pavement distress image classification in real time |
| title_full_unstemmed | GPU-enabled pavement distress image classification in real time |
| title_short | GPU-enabled pavement distress image classification in real time |
| title_sort | gpu-enabled pavement distress image classification in real time |
| url | https://eprints.nottingham.ac.uk/35192/ |