Tire Texture Image Recognition
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Tire Texture Image Recognition
Datasets
Tire Texture Image Recognition
File
Tire Texture Image Recognition
Use Case
Computer Vision
Description
This dataset is split into training and testing data, Which is further split into Cracked(Oxidized) and Normal Tires. It can be used for binary classification
About Dataset
Context
The dataset consists of 1028 images of tires in total.
Content
This dataset is split into training and testing data, Which is further split into Cracked(Oxidized) and Normal Tires. It can be used for binary classification
Acknowledgements
Citation
@data{DVN/Z3ZYLI_2021,
author = {Siegel, Joshua},
publisher = {Harvard Dataverse},
title = {{Oxidized and non-oxidized tire sidewall and tread images}},
year = {2021},
version = {V1},
doi = {10.7910/DVN/Z3ZYLI},
url = {Â https://doi.org/10.7910/DVN/Z3ZYLIÂ }
}
Enhanced Safety
By leveraging pattern model acceptance, you can prevent accidents caused by tire failures and improve vehicle performance. This technology aids in early detection of tread wear, sidewall damage, and other critical issues, allowing for timely maintenance and replacements. Enhance your fleet management with accurate and reliable tire assessments, reducing downtime and maintenance costs.
Industry Integration
Integrate pattern model acceptance into your automotive solutions to stay ahead in the industry. Whether you’re a manufacturer aiming to improve product quality or a service provider focused on enhancing safety, this technology offers unparalleled accuracy and reliability. Drive innovation and ensure the longevity of your tires with this state-of-the-art image recognition tool.
Conclusion
Invest in the future of automotive safety and performance by adopting Tire Texture Image Recognition technology today. This advanced solution promises to enhance vehicle safety, reduce maintenance costs, and improve overall performance, making it an essential tool for the modern automotive industry.
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