Tire Surface Inspection Dataset
#Object Detection #Image Classification #Tire Life Assessment #Automatic Tire Change Alerts #Tire Safety Warnings
- 15000 records
- 3.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-09-20
AI Analysis & Value Prop
The Tire Surface Inspection Dataset is developed to address the growing need for automated tire quality assessment in the automotive industry. With the increasing number of vehicles on the road, ensuring tire safety has become a critical challenge. Current manual inspection methods are time-consuming and prone to human error, leading to potential safety risks. This dataset aims to provide high-quality images for training machine learning models to automate tire inspection processes. Data is collected using high-resolution cameras in controlled environments, ensuring consistency. Quality control measures include multiple rounds of annotation and expert reviews to maintain high accuracy. The dataset is stored in JPG format, organized by defect types and severity levels to facilitate easy access and analysis.
Dataset Insights
Sample Examples
0b97a4e7**.jpg|1276*1616|181.57 KB
54355e9c**.jpg|1276*1616|227.08 KB
57674ebb**.jpg|1276*1616|225.17 KB
7ac9aa61**.jpg|1276*1616|250.21 KB
755a3f82**.jpg|1276*1616|409.54 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| tire_type | string | Types of tires, such as passenger car tires, truck tires, etc. |
| tire_condition | string | The surface condition of the tire, such as new, worn, etc. |
| defect_type | string | Types of defects on the tire surface, such as cracks, damage, bubbles, etc. |
| defect_position | string | Specific location of defects on the tire surface, such as side, tread, etc. |
| defect_size | float | The size or area of the defect, usually measured in square millimeters. |
| surface_texture | string | Texture characteristics of the tire surface, such as smooth, rough, etc. |
| object_position | string | The position of the tire object in the image, such as top left, center, bottom right, etc. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What is the Tire Surface Detection Dataset?
- The Tire Surface Detection Dataset is an object detection dataset containing high-quality images used to detect and identify defects and anomalies on tire surfaces in industrial environments.
- What type of data is included in this dataset?
- This dataset includes image data that focuses on the detection and identification of tire surfaces.
- What are the main application scenarios of the Tire Surface Detection Dataset?
- It is mainly applied in industrial automation detection, such as quality inspection during tire manufacturing processes.
- How does the Tire Surface Detection Dataset support industrial automation?
- By providing high-quality detection images, this dataset supports machine learning models in identifying defects and anomalies on tire surfaces, thus improving production efficiency in industrial automation.
- What advantages can be gained from using the Tire Surface Detection Dataset?
- Using this dataset can increase the accuracy and efficiency of detection, reduce the cost of manual inspections, and enhance product quality control.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Tire Surface Inspection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/f742492e8060bf129a7a79beda7510c5},
urldate={2025-08-28},
keywords={Tire Inspection Dataset,Automated Tire Quality Assessment,Tire Defect Detection,Machine Learning for Tire Safety},
version={1.0}
}Using this in research? Please cite us.
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