Please use this identifier to cite or link to this item: https://www.um.edu.mt/library/oar/handle/123456789/58892
Title: Automatic crack detection using Mask R-CNN
Authors: Attard, Leanne
Debono, Carl
Valentino, Gianluca
Di Castro, Mario
Masi, Alessandro
Scibile, Luigi
Keywords: Optical data processing
Machine learning
Issue Date: 2019
Publisher: IEEE
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.
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%.
URI: https://www.um.edu.mt/library/oar/handle/123456789/58892
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