Convolutional neural network and opencv based mobile application to detect wear out in car tyres / Harshitha Allipilli and Samyuktha Samala

Allipilli, Harshitha and Samala, Samyuktha (2021) Convolutional neural network and opencv based mobile application to detect wear out in car tyres / Harshitha Allipilli and Samyuktha Samala. Journal of Computing Research and Innovation (JCRINN), 6 (1). pp. 97-110. ISSN 2600-8793 (eISSN)

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Abstract

This work proposes a technique for detecting wear out of car tyres. Tyre is the only part of the vehicle which is in contact with road. Hence tyre condition should be monitored timely in order to have a safe drive. Tyre wear out occurs because of the parameters such as when the tread limit of tyre is less than 1.6 cm, rubber degradation, when there are around 4 to 5 punctures, bulged tyre. We consider some of the above parameters to assess the wear of tyre using the computer vision techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are most used in object detection and image classification. We used these techniques and obtained an accuracy of 90.95%, with which we can predict the wear of tyre to avoid dangerous accidents.

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Item Type: Article
Creators:
Creators
Email
Allipilli, Harshitha
allipilliharshita@gmail.com
Samala, Samyuktha
UNSPECIFIED
Subjects: Q Science > QA Mathematics > Online data processing
Q Science > QA Mathematics > Instruments and machines > Electronic Computers. Computer Science > Neural networks (Computer science)
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Computer and Mathematical Sciences
Journal or Publication Title: Journal of Computing Research and Innovation (JCRINN)
UiTM Journal Collections: UiTM Journal > Journal of Computing Research and Innovation (JCRINN)
ISSN: 2600-8793 (eISSN)
Volume: 6
Number: 1
Page Range: pp. 97-110
Official URL: https://doi.org/10.24191/jcrinn.v6i1.181
Item ID: 47090
Uncontrolled Keywords: open cv, convnet, convolutional neural networks
URI: https://ir.uitm.edu.my/id/eprint/47090

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47090

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