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DC Field | Value | Language |
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dc.contributor.author | Attard, Leanne | - |
dc.contributor.author | Debono, Carl James | - |
dc.contributor.author | Valentino, Gianluca | - |
dc.contributor.author | Di Castro, Mario | - |
dc.contributor.author | Masi, Alessandro | - |
dc.contributor.author | Scibile, Luigi | - |
dc.date.accessioned | 2020-07-17T09:54:28Z | - |
dc.date.available | 2020-07-17T09:54:28Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Attard, L., Debono, C. J., Valentino, G., Di Castro, M., Masi, A., & Scibile, L. (2019, September). Automatic crack detection using Mask R-CNN. In 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA) (pp. 152-157). IEEE. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/58892 | - |
dc.description.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%. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | IEEE | en_GB |
dc.rights | info:eu-repo/semantics/restrictedAccess | en_GB |
dc.subject | Optical data processing | en_GB |
dc.subject | Machine learning | en_GB |
dc.title | Automatic crack detection using Mask R-CNN | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 11th International Symposium on Image and Signal Processing and Analysis (ISPA) | en_GB |
dc.bibliographicCitation.conferenceplace | Dubrovnik, Croatia, 23-25/09/2019 | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - FacICTCCE |
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08868619.pdf Restricted Access | 581.31 kB | Adobe PDF | View/Open Request a copy |
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