Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/2/858 |
| _version_ | 1797437233195646976 |
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| author | Youngpil Kim Shinuk Yi Hyunho Ahn Cheol-Ho Hong |
| author_facet | Youngpil Kim Shinuk Yi Hyunho Ahn Cheol-Ho Hong |
| author_sort | Youngpil Kim |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T11:16:53Z |
| format | Article |
| id | doaj.art-6be9923f6d624606bc23d2022d9f3bf2 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T11:16:53Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-6be9923f6d624606bc23d2022d9f3bf22023-12-01T00:28:49ZengMDPI AGSensors1424-82202023-01-0123285810.3390/s23020858Accurate Crack Detection Based on Distributed Deep Learning for IoT EnvironmentYoungpil Kim0Shinuk Yi1Hyunho Ahn2Cheol-Ho Hong3Department of Information and Telecommunication Engineering, Incheon National University, 119, Academy-ro, Yeonsu-gu, Incheon 22012, Republic of KoreaMetaverse World Co., 134, Teheran-ro, Gangnam-gu, Seoul 06235, Republic of KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of KoreaSchool of Electrical and Electronics Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of KoreaDefects 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.https://www.mdpi.com/1424-8220/23/2/858crack detectionedge computingU-NetEfficient-Net |
| spellingShingle | Youngpil Kim Shinuk Yi Hyunho Ahn Cheol-Ho Hong Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment Sensors crack detection edge computing U-Net Efficient-Net |
| title | Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment |
| title_full | Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment |
| title_fullStr | Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment |
| title_full_unstemmed | Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment |
| title_short | Accurate Crack Detection Based on Distributed Deep Learning for IoT Environment |
| title_sort | accurate crack detection based on distributed deep learning for iot environment |
| topic | crack detection edge computing U-Net Efficient-Net |
| url | https://www.mdpi.com/1424-8220/23/2/858 |
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