Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Y. | - |
dc.contributor.author | Yi, S. | - |
dc.contributor.author | Ahn, H. | - |
dc.contributor.author | Hong, C.-H. | - |
dc.date.accessioned | 2023-10-05T00:40:11Z | - |
dc.date.available | 2023-10-05T00:40:11Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.issn | 1424-8220 | - |
dc.identifier.issn | 1424-3210 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67995 | - |
dc.description.abstract | 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. © 2023 by the authors. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | MDPI | - |
dc.title | Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment | - |
dc.type | Article | - |
dc.identifier.doi | 10.3390/s23020858 | - |
dc.identifier.bibliographicCitation | Sensors, v.23, no.2 | - |
dc.description.isOpenAccess | Y | - |
dc.identifier.wosid | 000916438100001 | - |
dc.identifier.scopusid | 2-s2.0-85146716992 | - |
dc.citation.number | 2 | - |
dc.citation.title | Sensors | - |
dc.citation.volume | 23 | - |
dc.type.docType | Article | - |
dc.publisher.location | 스위스 | - |
dc.subject.keywordAuthor | crack detection | - |
dc.subject.keywordAuthor | edge computing | - |
dc.subject.keywordAuthor | Efficient-Net | - |
dc.subject.keywordAuthor | U-Net | - |
dc.relation.journalResearchArea | Chemistry | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalResearchArea | Instruments & Instrumentation | - |
dc.relation.journalWebOfScienceCategory | Chemistry, Analytical | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.relation.journalWebOfScienceCategory | Instruments & Instrumentation | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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