Applications of mathematical morphology operators in civil infrastructures

Civil infrastructures require a permanent attention and maintenance from the moment of commissioning to moment of demolition. One important aspect which is mandatory to be taken into consideration is crack detection. Cracks can appear during the lifetime of the civil infrastructure and require speci...

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Bibliographic Details
Published in:Earth science informatics 2024-10, Vol.17 (5), p.4027-4033
Main Authors: Abrudan, Dumitru, Drăgulinescu, Ana-Maria, Vizireanu, Nicolae
Format: Article
Language:English
Subjects:
Cracks
Data collection
Datasets
Drone aircraft
Earth and Environmental Science
Earth Sciences
Earth System Sciences
Flaw detection
Image acquisition
Image filters
Image processing
Information Systems Applications (incl.Internet)
Infrastructure
Manpower
Mathematical morphology
Morphology
Neural networks
Ontology
Operators (mathematics)
Simulation and Modeling
Space Exploration and Astronautics
Space Sciences (including Extraterrestrial Physics
Unmanned aircraft
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Summary:Civil infrastructures require a permanent attention and maintenance from the moment of commissioning to moment of demolition. One important aspect which is mandatory to be taken into consideration is crack detection. Cracks can appear during the lifetime of the civil infrastructure and require specialized personal for assessment. Depending of the civil infrastructure, this operation can require specialized skills (such as climbing). To overcome this issue with regards to specialized manpower, image processing is used. Nowadays, images can be easily acquired using an unmanned aircraft vehicle system known also as a drone. The main advantages of a drone for civil infrastructure image acquisition are: i) it can be operated at different heights, ii) rapid data collection, iii) cost and time savings, iv) user-friendly interface.The main purpose of our paper resides in improving the accuracy of the pre-trained neural networks when noisy images are used in civil works. Throughout our research, we used a dataset which contains three classes of images: with cracks, without cracks and with noise. To remove the noise presented in images mathematical morphology operators (MMO) are used. Our results reveal that using opening operator filter on a dataset of images which present civil infrastructure cracks outperform the results obtained using only pre-trained neural networks. In addition, the computation time is reduced.
ISSN:1865-0473
1865-0481
DOI:10.1007/s12145-024-01379-3