Tire Texture Image Recognition

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

Tire Texture Image Recognition

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 = { https://doi.org/10.7910/DVN/Z3ZYLI }
}

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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