Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures
The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/22/8105 |
| _version_ | 1797547792864903168 |
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| author | Jung Jin Kim Ah-Ram Kim Seong-Won Lee |
| author_facet | Jung Jin Kim Ah-Ram Kim Seong-Won Lee |
| author_sort | Jung Jin Kim |
| collection | DOAJ |
| description | The damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement. |
| first_indexed | 2024-03-10T14:50:12Z |
| format | Article |
| id | doaj.art-e0b1356f2c6e4b428b30ef587a8e04b2 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:50:12Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-e0b1356f2c6e4b428b30ef587a8e04b22023-11-20T21:05:18ZengMDPI AGApplied Sciences2076-34172020-11-011022810510.3390/app10228105Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete StructuresJung Jin Kim0Ah-Ram Kim1Seong-Won Lee2Department of Mechanical Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, KoreaKorea Institute of Civil Engineering and Building Technology, 83 Goyang-daero, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10223, KoreaKorea Institute of Civil Engineering and Building Technology, 83 Goyang-daero, Ilsanseo-gu, Goyang-si, Gyeonggi-do 10223, KoreaThe damage investigation and inspection methods for infrastructures performed in small-scale (type III) facilities usually involve a visual examination by an inspector using surveying tools (e.g., cracking, crack microscope, etc.) in the field. These methods can interfere with the subjectivity of the inspector, which may reduce the objectivity and reliability of the record. Therefore, a new image analysis technique is needed to automatically detect cracks and analyze the characteristics of the cracks objectively. In this study, an image analysis technique using deep learning is developed to detect cracks and analyze characteristics (e.g., length, and width) in images for small-scale facilities. Three stages of image processing pipeline are proposed to obtain crack detection and its characteristics. In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. After deep learning-based detection, in the third stage, thinning and tracking algorithms are applied to analyze length and width of crack in the image. The performance of the proposed method was tested using various crack images with label and the results showed good performance of crack detection and its measurement.https://www.mdpi.com/2076-3417/10/22/8105concrete crackconcrete structureartificial neural networkconvolution neural network |
| spellingShingle | Jung Jin Kim Ah-Ram Kim Seong-Won Lee Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures Applied Sciences concrete crack concrete structure artificial neural network convolution neural network |
| title | Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures |
| title_full | Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures |
| title_fullStr | Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures |
| title_full_unstemmed | Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures |
| title_short | Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures |
| title_sort | artificial neural network based automated crack detection and analysis for the inspection of concrete structures |
| topic | concrete crack concrete structure artificial neural network convolution neural network |
| url | https://www.mdpi.com/2076-3417/10/22/8105 |
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