Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/92493
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Industrial and Systems Engineering | en_US |
dc.creator | Wang, Y | en_US |
dc.creator | Liu, M | en_US |
dc.creator | Zheng, P | en_US |
dc.creator | Yang, H | en_US |
dc.creator | Zou, J | en_US |
dc.date.accessioned | 2022-04-07T06:33:51Z | - |
dc.date.available | 2022-04-07T06:33:51Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/92493 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | © 2020 Elsevier Ltd. All rights reserved. | en_US |
dc.rights | © 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Wang, Y., Liu, M., Zheng, P., Yang, H., & Zou, J. (2020). A smart surface inspection system using faster R-CNN in cloud-edge computing environment. Advanced Engineering Informatics, 43, 101037 is available at https://doi.org/10.1016/j.aei.2020.101037 | en_US |
dc.subject | Automated surface inspection | en_US |
dc.subject | Cloud-edge computing | en_US |
dc.subject | Convolutional neural networks | en_US |
dc.subject | Smart product-service system | en_US |
dc.title | A smart surface inspection system using faster R-CNN in cloud-edge computing environment | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 43 | en_US |
dc.identifier.doi | 10.1016/j.aei.2020.101037 | en_US |
dcterms.abstract | Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNN-based image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster R-CNN algorithm in the cloud-edge computing environment. The faster R-CNN as a CNN-based object detection method can efficiently identify defects in complex product images and the cloud-edge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Advanced engineering informatics, Jan. 2020, v. 43, 101037 | en_US |
dcterms.isPartOf | Advanced engineering informatics | en_US |
dcterms.issued | 2020-01 | - |
dc.identifier.scopus | 2-s2.0-85078666892 | - |
dc.identifier.eissn | 1474-0346 | en_US |
dc.identifier.artn | 101037 | en_US |
dc.description.validate | 202204 bcvc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | a1289 | - |
dc.identifier.SubFormID | 44479 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China | en_US |
dc.description.pubStatus | Published | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Wang_Smart_Surface_Inspection.pdf | Pre-Published Version | 13.69 MB | Adobe PDF | View/Open |
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