Next Article in Journal
Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
Previous Article in Journal
Software Support for Discourse-Based Textual Information Analysis: A Systematic Literature Review and Software Guidelines in Practice
Open AccessArticle

Comparative Analysis among Discrete Fourier Transform, K-Means and Artificial Neural Networks Image Processing Techniques Oriented on Quality Control of Assembled Tires

Dyrecta Lab, Research Institute, Via Vescovo Simplicio 45, 70014 Conversano (BA), Italy
*
Author to whom correspondence should be addressed.
Information 2020, 11(5), 257; https://doi.org/10.3390/info11050257
Received: 21 April 2020 / Revised: 6 May 2020 / Accepted: 7 May 2020 / Published: 8 May 2020
(This article belongs to the Section Information Applications)
The present paper discusses a comparative application of image processing techniques, i.e., Discrete Fourier Transform, K-Means clustering and Artificial Neural Network, for the detection of defects in the industrial context of assembled tires. The used Artificial Neural Network technique is based on Long Short-Term Memory and Fully Connected neural networks. The investigations focus on the monitoring and quality control of defects, which may appear on the external surface of tires after being assembled. Those defects are caused from tires which are not properly assembled to their respective metallic wheel rim, generating deformations and scrapes which are not desired. The proposed image processing techniques are applied on raw high-resolution images, which are acquired by in-line imaging and optical instruments. All the described techniques, i.e., Discrete Fourier Transform, K-Means clustering and Long Short-Term Memory, were able to determine defected and acceptable external tire surfaces. The proposed research is taken in the context of an industrial project which focuses on the development of automated quality control and monitoring methodologies, within the field of Industry 4.0 facilities. The image processing techniques are thus meant to be adopted into production processes, giving a strong support to the in-line quality control phase. View Full-Text
Keywords: In-line image processing; product traceability; image features extraction; 3D image processing; real time production monitoring; Discrete Fourier Transform; K-Means image processing; neural network In-line image processing; product traceability; image features extraction; 3D image processing; real time production monitoring; Discrete Fourier Transform; K-Means image processing; neural network
Show Figures

Figure 1

MDPI and ACS Style

Massaro, A.; Dipierro, G.; Cannella, E.; Galiano, A.M. Comparative Analysis among Discrete Fourier Transform, K-Means and Artificial Neural Networks Image Processing Techniques Oriented on Quality Control of Assembled Tires. Information 2020, 11, 257.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop