Accurate Crack Detection Based on Distributed Deep Learning for IoT Environmentopen access
- Authors
- Kim, Y.; Yi, S.; Ahn, H.; Hong, C.-H.
- Issue Date
- Jan-2023
- Publisher
- MDPI
- Keywords
- crack detection; edge computing; Efficient-Net; U-Net
- Citation
- Sensors, v.23, no.2
- Journal Title
- Sensors
- Volume
- 23
- Number
- 2
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67995
- DOI
- 10.3390/s23020858
- ISSN
- 1424-8220
1424-3210
- 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.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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