Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model
Cracks in the concrete surfaces are typically clear warning signs of a potential threat to the integrity and serviceability of the structure. The techniques based on image processing can effectively detect cracks in digital images. These techniques, however, are generally susceptible to user-driven...
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| Format: | Article |
| Language: | English |
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Gruppo Italiano Frattura
2023-07-01
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| Series: | Fracture and Structural Integrity |
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| Online Access: | https://www.fracturae.com/index.php/fis/article/view/4216/3845 |
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| author | Lin Wang |
| author_facet | Lin Wang |
| author_sort | Lin Wang |
| collection | DOAJ |
| description | Cracks in the concrete surfaces are typically clear warning signs of a potential threat to the integrity and serviceability of the structure. The techniques based on image processing can effectively detect cracks in digital images. These techniques, however, are generally susceptible to user-driven heuristic thresholds and irrelevant distractors. Inspired by the recent success of artificial intelligence, a deep learning-based automated crack detection system named CrackSN was presented. This proposed deep learning model, built on the Adam-SqueezeNet architecture, automatically learns the discriminative feature directly from the labeled and augmented patches. An image dataset of concrete surfaces is collected by smartphone and carefully prepared in order to develop and train the CrackSN system. The hyper-parameters of SqueezeNet are tuned with Adam optimization through the training and validation procedures. The fine-tuned CrackSN system outperforms state-of-the-art models in recent literature by correctly classifying 97.3% of the cracked patches in the image dataset. The success of CrackSN, demonstrated by its light network design and outstanding performance, provides a crucial step toward automated damage inspection and health evaluation for infrastructure |
| format | Article |
| id | doaj-art-afa0777a5aed4f9c83dcb2ae01445775 |
| institution | Kabale University |
| issn | 1971-8993 |
| language | English |
| publishDate | 2023-07-01 |
| publisher | Gruppo Italiano Frattura |
| record_format | Article |
| series | Fracture and Structural Integrity |
| spelling | doaj-art-afa0777a5aed4f9c83dcb2ae014457752025-01-02T23:01:26ZengGruppo Italiano FratturaFracture and Structural Integrity1971-89932023-07-01176528929910.3221/IGF-ESIS.65.1910.3221/IGF-ESIS.65.19Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning modelLin WangCracks in the concrete surfaces are typically clear warning signs of a potential threat to the integrity and serviceability of the structure. The techniques based on image processing can effectively detect cracks in digital images. These techniques, however, are generally susceptible to user-driven heuristic thresholds and irrelevant distractors. Inspired by the recent success of artificial intelligence, a deep learning-based automated crack detection system named CrackSN was presented. This proposed deep learning model, built on the Adam-SqueezeNet architecture, automatically learns the discriminative feature directly from the labeled and augmented patches. An image dataset of concrete surfaces is collected by smartphone and carefully prepared in order to develop and train the CrackSN system. The hyper-parameters of SqueezeNet are tuned with Adam optimization through the training and validation procedures. The fine-tuned CrackSN system outperforms state-of-the-art models in recent literature by correctly classifying 97.3% of the cracked patches in the image dataset. The success of CrackSN, demonstrated by its light network design and outstanding performance, provides a crucial step toward automated damage inspection and health evaluation for infrastructurehttps://www.fracturae.com/index.php/fis/article/view/4216/3845concrete crackautomated damage inspectionsqueezenetadam optimizationdeep learning |
| spellingShingle | Lin Wang Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model Fracture and Structural Integrity concrete crack automated damage inspection squeezenet adam optimization deep learning |
| title | Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model |
| title_full | Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model |
| title_fullStr | Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model |
| title_full_unstemmed | Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model |
| title_short | Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model |
| title_sort | automatic detection of concrete cracks from images using adam squeezenet deep learning model |
| topic | concrete crack automated damage inspection squeezenet adam optimization deep learning |
| url | https://www.fracturae.com/index.php/fis/article/view/4216/3845 |
| work_keys_str_mv | AT linwang automaticdetectionofconcretecracksfromimagesusingadamsqueezenetdeeplearningmodel |