Components Detection using Faster Regional-Based Convolutional Neural Network

Mervyn Meow Zi Yang*, The Peh Chiong**, Lim Siew Kee***
*-***Department of Electronic Engineering, University Tunku Abdul Rahman, Malaysia.
Periodicity:July - December'2021
DOI : https://doi.org/10.26634/jpr.8.2.16361

Abstract

The object detection task is one of the most popular examples of an artificial intelligence (AI) system that is used to identify and classify objects. The Faster Regional-based Convolutional Neural Network (FRCNN) was used to classify LEDs that were placed on any surface. This paper will entail the development of a deep learning model running on the Tensorflow Graphical Processing Unit (GPU) that is capable of identifying and classifying the SMT LED components accurately in realtime form. A different amount of dataset to train a deep learning model is used to make a comparison in terms of accuracy.

Keywords

Convolutional Neural Network (CNN), RCNN, Fast RCNN, and Faster RCNN.

How to Cite this Article?

Yang, M. M. Z., Chiong, T. P., and Kee, L. S. (2021). Components Detection using Faster Regional-Based Convolutional Neural Network. i-manager's Journal on Pattern Recognition, 8(2), 1-19. https://doi.org/10.26634/jpr.8.2.16361

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