Tire Valve Detection Dataset
#Object Detection #Image Classification #Tire Pressure Monitoring #Quality Inspection #Manufacturing
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-12
AI Analysis & Value Prop
The Tire Valve Detection Dataset addresses the challenges of tire pressure monitoring systems calibration and the identification of missing tire valves, which are critical for ensuring vehicle safety and performance. Current solutions often lack precision in detecting valve presence, which can lead to significant safety risks. This dataset aims to improve the accuracy of detection algorithms in manufacturing quality inspections and tire maintenance. Data collection was conducted using high-resolution cameras in controlled environments, ensuring optimal lighting and focus on tire valves. The dataset underwent strict quality control measures, including multiple rounds of annotation by trained personnel and consistency checks by experts. Images are stored in JPG format, organized by categories such as 'present' and 'missing' valves for easy access and analysis. This structured approach guarantees high reliability and usability of the dataset.
Dataset Insights
Sample Examples
2ec8f678**.jpg|1280*1601|255.13 KB
4c8334a8**.png|1280*1597|1.69 MB
9f8445c7**.jpg|1280*1525|168.35 KB
ca7b10dc**.jpg|1280*960|188.66 KB
0cb6eddf**.jpg|1280*720|194.02 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| tire_valve_presence | boolean | Whether the valve stem is present in the image. |
| valve_orientation | string | The orientation of the tire pressure nozzle in the image, described as text (e.g., 'horizontal', 'vertical'). |
| valve_condition | string | The physical condition of the tire pressure nozzle (e.g., 'intact', 'damaged'). |
| valve_type | string | The category or model of the tire pressure nozzle. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| 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 Valve Detection Dataset?
- The Tire Valve Detection Dataset is a target detection dataset focused on identifying and detecting tire valves of vehicles, primarily used in industrial quality inspection scenarios.
- In which fields is the Tire Valve Detection Dataset applicable?
- The Tire Valve Detection Dataset is mainly applicable in the industrial field, especially in the automotive manufacturing and maintenance industry, assisting in the quality inspection of vehicle tire valves.
- What are the benefits of using the Tire Valve Detection Dataset?
- Using the Tire Valve Detection Dataset can improve the efficiency and accuracy of industrial quality inspection, helping to timely detect defects in tire valves.
- What types of image data are included in the Tire Valve Detection Dataset?
- The Tire Valve Detection Dataset includes images of tire valves taken from various angles and under different lighting conditions, to help train models for object detection.
- How can the Tire Valve Detection Dataset be utilized in industrial applications?
- In industrial applications, the Tire Valve Detection Dataset can be used to train machine learning models to automatically check the compliance of tire valves in production lines or maintenance scenarios.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Tire Valve Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/7c84ed79e8167b61340fee542a674a97},
urldate={2025-08-28},
keywords={Tire Valve Detection,Tire Pressure Monitoring Dataset,Quality Inspection Dataset,Industrial Image Dataset},
version={1.0}
}Using this in research? Please cite us.
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