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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Format: | Article |
Language: | English |
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Nature Portfolio
2023-05-01
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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. |
first_indexed | 2024-03-13T10:16:03Z |
format | Article |
id | doaj.art-b24f8afb9d3645ab9cb48dc7bcb8daa0 |
institution | Directory Open Access Journal |
issn | 2045-2322 |
language | English |
last_indexed | 2024-03-13T10:16:03Z |
publishDate | 2023-05-01 |
publisher | Nature Portfolio |
record_format | Article |
series | Scientific Reports |
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 |