Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment.
In: Sensors (14248220), Jg. 23 (2023-01-15), Heft 2, S. 858-869
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Zugriff:
Defects or cracks in roads, building walls, floors, and product surfaces can degrade the completeness of the product and become an impediment to quality control. Machine learning can be a solution for detecting defects effectively without human experts; however, the low-power computing device cannot afford that. In this paper, we suggest a crack detection system accelerated by edge computing. Our system consists of two: Rsef and Rsef-Edge. Rsef is a real-time segmentation method based on effective feature extraction that can perform crack image segmentation by optimizing conventional deep learning models. Then, we construct the edge-based system, named Rsef-Edge, to significantly decrease the inference time of Rsef, even in low-power IoT devices. As a result, we show both a fast inference time and good accuracy even in a low-powered computing environment. [ABSTRACT FROM AUTHOR]
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Titel: |
Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment.
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Autor/in / Beteiligte Person: | Kim, Youngpil ; Yi, Shinuk ; Ahn, Hyunho ; Hong, Cheol-Ho |
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Zeitschrift: | Sensors (14248220), Jg. 23 (2023-01-15), Heft 2, S. 858-869 |
Veröffentlichung: | 2023 |
Medientyp: | academicJournal |
ISSN: | 1424-8220 (print) |
DOI: | 10.3390/s23020858 |
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