Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction
The crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton i...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
|
| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/6942295 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841561348191813632 |
|---|---|
| author | Rui Wang Xinxin Guo Jiaxuan Liu Yu Wan |
| author_facet | Rui Wang Xinxin Guo Jiaxuan Liu Yu Wan |
| author_sort | Rui Wang |
| collection | DOAJ |
| description | The crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton is proposed in this paper. The main processing steps include image preprocessing, crack skeleton extraction, and inflection point recognition. In extracting the crack skeleton, the skeleton is initially extracted using morphological operations. After identifying the endpoints based on the neighborhood distribution of the skeleton points, the burrs are tracked until the branching point using the endpoints as the starting point, and the skeleton burrs are judged and removed by setting a threshold with a burr removal rate of 100%. For the identification of inflection points based on chain code calculation, the algorithm is optimized by the concentration of the inflection points and the distance and angle between the inflection points to remove false inflection points. The test data show that the algorithm has good adaptability and the accuracy is higher than 90%. After obtaining the crack skeleton image with real inflection points, the structural features of the skeleton can be calculated, including the distance ratio before and after the inflection points and the angle formed between the inflection points. This lays the foundation for future research to realize the recognition of the same crack at different time points. |
| format | Article |
| id | doaj-art-93545d3647c14c95bfcb171ee731e163 |
| institution | Kabale University |
| issn | 1687-8094 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-93545d3647c14c95bfcb171ee731e1632025-01-03T01:43:58ZengWileyAdvances in Civil Engineering1687-80942024-01-01202410.1155/2024/6942295Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature ExtractionRui Wang0Xinxin Guo1Jiaxuan Liu2Yu Wan3College of EngineeringCollege of Environment and Civil EngineeringCollege of EngineeringCollege of Civil EngineeringThe crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton is proposed in this paper. The main processing steps include image preprocessing, crack skeleton extraction, and inflection point recognition. In extracting the crack skeleton, the skeleton is initially extracted using morphological operations. After identifying the endpoints based on the neighborhood distribution of the skeleton points, the burrs are tracked until the branching point using the endpoints as the starting point, and the skeleton burrs are judged and removed by setting a threshold with a burr removal rate of 100%. For the identification of inflection points based on chain code calculation, the algorithm is optimized by the concentration of the inflection points and the distance and angle between the inflection points to remove false inflection points. The test data show that the algorithm has good adaptability and the accuracy is higher than 90%. After obtaining the crack skeleton image with real inflection points, the structural features of the skeleton can be calculated, including the distance ratio before and after the inflection points and the angle formed between the inflection points. This lays the foundation for future research to realize the recognition of the same crack at different time points.http://dx.doi.org/10.1155/2024/6942295 |
| spellingShingle | Rui Wang Xinxin Guo Jiaxuan Liu Yu Wan Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction Advances in Civil Engineering |
| title | Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction |
| title_full | Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction |
| title_fullStr | Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction |
| title_full_unstemmed | Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction |
| title_short | Concrete Crack Skeleton Analysis: A Machine Vision Approach to Feature Extraction |
| title_sort | concrete crack skeleton analysis a machine vision approach to feature extraction |
| url | http://dx.doi.org/10.1155/2024/6942295 |
| work_keys_str_mv | AT ruiwang concretecrackskeletonanalysisamachinevisionapproachtofeatureextraction AT xinxinguo concretecrackskeletonanalysisamachinevisionapproachtofeatureextraction AT jiaxuanliu concretecrackskeletonanalysisamachinevisionapproachtofeatureextraction AT yuwan concretecrackskeletonanalysisamachinevisionapproachtofeatureextraction |