Research on tire crack detection using image deep learning method

Abstract Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection metho...

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Main Author: Shih-Lin Lin
Format: Article
Language:English
Published: Nature Portfolio 2023-05-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-023-35227-z
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author Shih-Lin Lin
author_facet Shih-Lin Lin
author_sort Shih-Lin Lin
collection DOAJ
description Abstract Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection method was designed. This paper improves the traditional ShuffleNet and proposes an improved ShuffleNet method for tire image detection. The research results are compared with the five methods of GoogLeNet, traditional ShuffleNet, VGGNet, ResNet and improved ShuffleNet through tire database verification. The experiment found that the detection rate of tire debris defects was 94.7%. Tire defects can be effectively detected, which proves the robustness and effectiveness of the improved ShuffleNet, enabling drivers and tire manufacturers to save labor costs and greatly reduce tire defect detection time.
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spelling doaj.art-b24f8afb9d3645ab9cb48dc7bcb8daa02023-05-21T11:14:41ZengNature PortfolioScientific Reports2045-23222023-05-0113111710.1038/s41598-023-35227-zResearch on tire crack detection using image deep learning methodShih-Lin Lin0Graduate Institute of Vehicle Engineering, National Changhua University of EducationAbstract Driving can understand the importance of tire tread depth and air pressure, but most people are unaware of the safety risks of tire oxidation. Drivers must maintain vehicle tire quality to ensure performance, efficiency, and safety. In this study, a deep learning tire defect detection method was designed. This paper improves the traditional ShuffleNet and proposes an improved ShuffleNet method for tire image detection. The research results are compared with the five methods of GoogLeNet, traditional ShuffleNet, VGGNet, ResNet and improved ShuffleNet through tire database verification. The experiment found that the detection rate of tire debris defects was 94.7%. Tire defects can be effectively detected, which proves the robustness and effectiveness of the improved ShuffleNet, enabling drivers and tire manufacturers to save labor costs and greatly reduce tire defect detection time.https://doi.org/10.1038/s41598-023-35227-z
spellingShingle Shih-Lin Lin
Research on tire crack detection using image deep learning method
Scientific Reports
title Research on tire crack detection using image deep learning method
title_full Research on tire crack detection using image deep learning method
title_fullStr Research on tire crack detection using image deep learning method
title_full_unstemmed Research on tire crack detection using image deep learning method
title_short Research on tire crack detection using image deep learning method
title_sort research on tire crack detection using image deep learning method
url https://doi.org/10.1038/s41598-023-35227-z
work_keys_str_mv AT shihlinlin researchontirecrackdetectionusingimagedeeplearningmethod