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On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

Aslam, M, Khan, Tariq, Naqvi, SS, Holmes, G and Naffa, R 2019, On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey, IEEE Access, vol. 7, pp. 176065-176086, doi: 10.1109/ACCESS.2019.2957427.

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Title On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey
Author(s) Aslam, M
Khan, TariqORCID iD for Khan, Tariq orcid.org/0000-0002-7477-1591
Naqvi, SS
Holmes, G
Naffa, R
Journal name IEEE Access
Volume number 7
Start page 176065
End page 176086
Total pages 22
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2019-12-03
ISSN 2169-3536
Keyword(s) leather defects
segmentation
classification
machine learning
computer vision
Summary Reliably and effectively detecting and classifying leather surface defects is of great importance to tanneries and industries that use leather as a major raw material such as leather footwear and handbag manufacturers. This paper presents a detailed and methodical review of the leather surface defects, their effects on leather quality grading and automated visual inspection methods for leather defect inspection. A detailed review of inspection methods based on leather defect detection using image analysis methods is presented, which are usually classified as heuristic or basic machine learning based methods. Due to the recent success of deep learning methods in various related fields, various architectures of deep learning are discussed that are tailored to image classification, detection, and segmentation. In general, visual inspection applications, where recent CNN architectures are classified, compared, and a detailed review is subsequently presented on the role of deep learning methods in leather defect detection. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather quality quantification, and simultaneous defect inspection methods, which need to be investigated in the future to make progress in this crucial area of research.
Language eng
DOI 10.1109/ACCESS.2019.2957427
Indigenous content off
Field of Research 08 Information and Computing Sciences
09 Engineering
10 Technology
HERDC Research category C1.1 Refereed article in a scholarly journal
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146635

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.