%0 Journal Article
	%A Fidel Lòpez Saca and  Carlos Avilés-Cruz and  Miguel Magos-Rivera and  José Antonio Lara-Chávez
	%D 2020
	%J International Journal of Educational and Pedagogical Sciences
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 166, 2020
	%T Automatic Product Identification Based on Deep-Learning Theory in an Assembly Line
	%U https://publications.waset.org/pdf/10011523
	%V 166
	%X Automated object recognition and identification systems
are widely used throughout the world, particularly in assembly lines,
where they perform quality control and automatic part selection tasks.
This article presents the design and implementation of an object
recognition system in an assembly line. The proposed shapes-color
recognition system is based on deep learning theory in a specially
designed convolutional network architecture. The used methodology
involve stages such as: image capturing, color filtering, location
of object mass centers, horizontal and vertical object boundaries,
and object clipping. Once the objects are cut out, they are sent to
a convolutional neural network, which automatically identifies the
type of figure. The identification system works in real-time. The
implementation was done on a Raspberry Pi 3 system and on a
Jetson-Nano device. The proposal is used in an assembly course
of bachelor’s degree in industrial engineering. The results presented
include studying the efficiency of the recognition and processing time.
	%P 994 - 1001