Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
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Basel: MDPI AG
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Language
English
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Basel: MDPI AG
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This paper introduces and implements an efficient training method for deep learning–based anomaly area detection in the depth image of a tire. A depth image of 16 bit integer size is used in various fields, such as manufacturing, industry, and medicine. In addition, the advent of the 4th Industrial Revolution and the development of deep learning re...
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Anomaly Segmentation Based on Depth Image for Quality Inspection Processes in Tire Manufacturing
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TN_cdi_doaj_primary_oai_doaj_org_article_f566b8493874411788ce35d4ea748587
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_f566b8493874411788ce35d4ea748587
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ISSN
2076-3417
E-ISSN
2076-3417
DOI
10.3390/app112110376
