Tyre Inspection through Multi-State Convolutional Neural Networks C. Sivamani 1 , M. Rajeswari 2 , E. Golden Julie 3 , Y. Harold Robinson 4 , Vimal Shanmuganathan 5 , Seifedine Kadry 6 and Yunyoung Nam 7,* 1 BMIE, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, 641043, India 2 Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Kerala, 680684, India 3 Department of Computer Science and Engineering, Anna University Regional Campus, Tirunelveli, 627007, India 4 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India 5 Department of Information Technology, National Engineering College, Kovilpatti, 628503, India 6 Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, 115020, Lebanon 7 Department of Computer Science and Engineering, Soonchunhyang University, Asan, 336745, Korea  Corresponding Author: Yunyoung Nam. Email: ynam@sch.ac.kr Received: 18 August 2020; Accepted: 23 October 2020 Abstract: Road accident is a potential risk to the lives of both drivers and passers- by. Many road accidents occur due to the improper condition of the vehicle tyres after long term usage. Thus, tyres need to be inspected and analyzed while man- ufacturing to avoid serious road problems. However, tyre wear is a multifaceted happening. It normally needs the non-linearly on many limitations, like tyre for- mation and plan, vehicle category, conditions of the road. Yet, tyre wear has numerous profitable and environmental inferences particularly due to mainte- nance costs and traffic safety implications. Thus, the risk to calculate tyre wear is therefore of major importance to tyre producers, convoy owners and govern- ment. In this paper, we propose a Multi-state Convolution Neural Networks to analyze tyre tread patterns about wear and tear as well as tyre durability. The fea- ture maps are identified from the input image through the Convolution functions that the sub-sampling utilizes for producing the output with the fully connected networks. The quadratic surface uses to perform the preprocessing of tyre images with several Convolutional layers. Through this work, we aim to reduce the eco- nomic implications as well as traffic safety implications which happen due to tyre wear. This will serve as a potential solution to tyre wear-related issues. Keywords: Convolutional neural networks; machine learning; deep learning; tyre wear prediction 1 Introduction In recent days, AI together with deep learning and machine learning invites many researchers to focus on solving numerous numbers of real-world problems. There are multiple ways for implementing machine learning classification for which the algorithms and techniques used are reliant on the problem to be solved and the dataset used. It will take a long time to study and analyze all kinds of machine learning classifiers. Characteristically, it is expensive and period overshadowing to construct a classifier, since this This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Intelligent Automation & Soft Computing DOI:10.32604/iasc.2021.013705 Article ech T Press Science