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Quality control of knurled parts by using machine vision [Elektronski vir]Marolt, Jakob, strojnikIntroduction: In this article the processes behind prototyping machine vision system has been described for inspecting the quality of knurled parts that could be used as a part of greater ... intralogistics system in the manufacturing firm. Method: The processes behind three popular feature matching algorithms (SIFT, SURF and ORB) were explained. For data processing Raspberry Pi model 1 B+ was used, which ran on the Raspbian Debian system. For lighting two white LED diodes with high brightness were installed. Pictures were taken by a standard Raspberry Pi CMOS camera with 5 MP. The program was created in Python by using its standard modules and the OpenCV library. Results and discussion: Success and time delay of all three feature matching algorithms were analysed. All were 100% successful in distinguishing the good parts from the bad ones. The fastest algorithm was ORB, followed by SURF and then SIFT. The material cost of the system was 87.00 EUR.Source: Proceedings [Elektronski vir] (Str. 5-12)Type of material - conference contributionPublish date - 2018Language - englishCOBISS.SI-ID - 512907069
Author
Marolt, Jakob, strojnik
Topics
production logistics |
machine vision |
quality control |
OpenCV |
knurling
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Marolt, Jakob, strojnik | 50939 |
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