Automatic Crack Detection using Mask R-CNN
In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage...
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| Published in: | 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) pp. 152 - 157 |
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| Main Authors: | , , , , , |
| Format: | Conference Proceeding |
| Language: | English |
| Published: |
IEEE
01.09.2019
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| Subjects: | |
| ISSN: | 1849-2266 |
| Online Access: | Get full text |
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| Abstract | In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage. In this paper, we demonstrate that Mask R-CNN can be used to localize cracks on concrete surfaces and obtain their corresponding masks to aid extract other properties that are useful for inspection. Such a tool can help mitigate the drawbacks of manual inspection by automating crack detection, lowering time consumption in executing this task, reducing costs and increasing the safety of the personnel. To train Mask R-CNN for crack detection we built a groundtruth database of masks on images from a subset of a standard crack dataset. Tests on the trained model achieved a precision value of 93.94 % and a recall of 77.5 %. |
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| AbstractList | In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis. Cracks are one of the earliest indications of degradation, hence, their detection allows preventive measures to be taken to avoid further damage. In this paper, we demonstrate that Mask R-CNN can be used to localize cracks on concrete surfaces and obtain their corresponding masks to aid extract other properties that are useful for inspection. Such a tool can help mitigate the drawbacks of manual inspection by automating crack detection, lowering time consumption in executing this task, reducing costs and increasing the safety of the personnel. To train Mask R-CNN for crack detection we built a groundtruth database of masks on images from a subset of a standard crack dataset. Tests on the trained model achieved a precision value of 93.94 % and a recall of 77.5 %. |
| Author | Scibile, Luigi Masi, Alessandro Di Castro, Mario Valentino, Gianluca Debono, Carl James Attard, Leanne |
| Author_xml | – sequence: 1 givenname: Leanne surname: Attard fullname: Attard, Leanne email: leanne.attard@um.edu.mt organization: University of Malta, Msida, Malta – sequence: 2 givenname: Carl James surname: Debono fullname: Debono, Carl James email: c.debono@ieee.org organization: University of Malta, Msida, Malta – sequence: 3 givenname: Gianluca surname: Valentino fullname: Valentino, Gianluca email: gianluca.valentino@um.edu.mt organization: University of Malta, Msida, Malta – sequence: 4 givenname: Mario surname: Di Castro fullname: Di Castro, Mario email: mario.di.castro@cern.ch organization: Survey, Mechatronics and Measurements group, CERN, Meyrin, Switzerland – sequence: 5 givenname: Alessandro surname: Masi fullname: Masi, Alessandro email: alessandro.masi@cern.ch organization: Survey, Mechatronics and Measurements group, CERN, Meyrin, Switzerland – sequence: 6 givenname: Luigi surname: Scibile fullname: Scibile, Luigi email: luigi.scibile@cern.ch organization: CERN, Meyrin, Switzerland |
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| Snippet | In order to avoid possible failures and prevent damage in civil infrastructures, such as tunnels and bridges, inspection should be done on a regular basis.... |
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| StartPage | 152 |
| SubjectTerms | crack detection Feature extraction Inspection mask r-cnn object detection Pipelines Signal processing Surface cracks Surface morphology Training vision-based inspection |
| Title | Automatic Crack Detection using Mask R-CNN |
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