Deployment of Machine Vision Platform for Checking Spot Welds on Metal Strap Belts

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dc.contributor.authorWiangtong, Theerayod
dc.contributor.authorWongkharn, Siripong
dc.contributor.authorSirisuk, Phaophak
dc.date.accessioned2026-08-06T10:39:58Z
dc.date.available2026-08-06T10:39:58Z
dc.date.issued2023-01-01
dc.description.abstractThis paper presents a designed platform used to detect the perfection and number of spot welds on the strap belt of metal sheet coils. Three different approaches include image morphology, thresholding and Hough transform are compared. The results from real implementation show that using the adaptive threshold values in image thresholding approach instead of fixed value can increase the system accuracy from 69% to 88%. Also, to find the pad, the comparison of using Haar cascade machine learning and YOLO deep learning is described.
dc.identifier.citationProceeding 2023 International Electrical Engineering Congress Ieecon 2023, 224-228, 2023
dc.identifier.doi10.1109/iEECON56657.2023.10126530
dc.identifier.other2-s2.0-85162969773
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/14014
dc.sourceProceeding 2023 International Electrical Engineering Congress Ieecon 2023
dc.subjectA coil of metal sheet
dc.subjectHaar cascade
dc.subjectObject detection
dc.subjectSpot welding
dc.subjectVision inspection
dc.subjectYOLO
dc.titleDeployment of Machine Vision Platform for Checking Spot Welds on Metal Strap Belts
dc.typeConference Paper

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