Part Marking Detection Using Machine Learning

Radha Ramesh Tiwari , Sangita Bharkad

Image Processing, Object Detection, Faster R- CNN, Machine Learning, Deep Learning.

For Paper presents Faster R-CNN based deep learning implementation of numbering of mechanical parts such as gears. Parts are photographed in real time. In this research works the parts are sorted into three categories- the correctly numbered parts, non-numbered parts and over-ride numbered parts- through image processing followed by deep learning algorithm. For this work Mobile-Net Model on TensorFlow Machine Learning platform to accomplish part identification. Visual inspection validates the technique to 95% accuracy in real time detection.

Published Paper   E-Certificate

"Part Marking Detection Using Machine Learning", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 10, page no.385 - 390, October-2020, Available :https://ijsdr.org/papers/IJSDR2010059.pdf

Volume 5 Issue 10, October-2020

Pages : 385 - 390

Paper Reg. ID: IJSDR_192661

Published Paper Id: IJSDR2010059

Downloads: 000347187

Research Area: Engineering

Country: Aurangabad, maharashtra, India

Published Paper PDF: https://ijsdr.org/papers/IJSDR2010059

Published Paper URL: https://ijsdr.org/viewpaperforall?paper=IJSDR2010059

ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJSDR(IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition