Orinaitė, U.; Karaliūtė, V.; Pal, M.; Ragulskis, M. Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem. Appl. Sci.2023, 13, 7335.
Orinaitė, U.; Karaliūtė, V.; Pal, M.; Ragulskis, M. Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem. Appl. Sci. 2023, 13, 7335.
Orinaitė, U.; Karaliūtė, V.; Pal, M.; Ragulskis, M. Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem. Appl. Sci.2023, 13, 7335.
Orinaitė, U.; Karaliūtė, V.; Pal, M.; Ragulskis, M. Detecting Underwater Concrete Cracks with Machine Learning: A Clear Vision of a Murky Problem. Appl. Sci. 2023, 13, 7335.
Abstract
This paper presents the development of an underwater crack detection system for structural integrity assessment of submerged structures, like offshore oil and gas installations, underwater pipelines, underwater foundations for bridges, dams etc. Focus is on use of machine learning based approaches. First a detailed literature review of state of the current methods for underwater surface crack detection is presented highlighting challenges and opportunities. An overview of image augmentation approach for creation of underwater optical effects is also presented. Experimental results using standard network based machine learning approach, used for surface crack detection in onshore environment, is presented. Series of Test cases are presented where existing networks performance are improved using augmented images for underwater conditions. The experimental results demonstrate the effectiveness and accuracy of the proposed system in detecting cracks in underwater structures. The system has the potential to improve the safety and reliability of underwater structures and prevent catastrophic failures.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
