Tunnel Traffic Control Sign Recognition Dataset
#Target Detection #Image Recognition #Traffic Monitoring #Intelligent Transportation #Autonomous Driving
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-12
AI Analysis & Value Prop
With the rapid development of urban traffic, the technology for automatic recognition of traffic control signs faces severe challenges, especially in complex tunnel environments where recognition rates are often affected by factors such as lighting and occlusion. Existing solutions rely mostly on manual recognition, which is inefficient and prone to errors. Therefore, establishing a high-quality tunnel traffic control sign recognition dataset will effectively enhance the application of target detection algorithms in this field. The dataset includes several types of traffic control signs, aiming to address the low recognition accuracy of existing technology in complex environments. Data collection is conducted using high-resolution cameras in actual tunnels, ensuring the authenticity and diversity of images. Meanwhile, data annotation follows a strict quality control process, including multiple rounds of annotation and expert review, to ensure consistency and accuracy of annotations. Data is stored in JPEG format, organized by category and time for easy retrieval and use.
Dataset Insights
Sample Examples
656673d3**.jpg|3072*4096|702.23 KB
4ca56916**.jpg|1440*1920|177.87 KB
8201d938**.jpg|1440*1920|321.54 KB
9a426e09**.jpg|1440*1920|341.40 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| sign_type | string | The specific type of traffic control sign, such as speed limit signs or no-entry signs. |
| sign_color | string | The primary color of the sign, such as red, blue, or green. |
| sign_shape | string | The geometric shape of the sign, like circular, triangular, or rectangular. |
| sign_text | string | The textual content displayed on the sign. |
| sign_position | string | The description of the sign's location in the image, such as central position or corner coordinates. |
| background_complexity | string | The complexity level of the image background, such as simple, moderate, or complex. |
| illumination_conditions | string | The lighting conditions during the capture, such as well-lit, under-lit, or nighttime conditions. |
| weather_conditions | string | The weather conditions at the time of image capture, such as sunny, rainy, or foggy. |
| visibility_level | string | The visibility level of the sign in the image, such as clear, blurred, or unclear. |
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 Tunnel Traffic Control Sign Recognition Dataset?
- The Tunnel Traffic Control Sign Recognition Dataset is an image dataset focused on recognizing traffic control signs within tunnels to promote intelligent traffic systems.
- What industries is this dataset suitable for?
- This dataset is mainly applicable to the transportation industry, especially in the field of tunnel traffic automation and intelligent management.
- What is the main use of the Tunnel Traffic Control Sign Recognition Dataset?
- This dataset is used to develop and train machine learning models for automatically recognizing traffic control signs in tunnels, enhancing traffic management efficiency.
- Why is tunnel traffic control sign recognition important for traffic intelligence?
- Recognizing tunnel traffic control signs aids in automated navigation and traffic flow optimization, improving safety and efficiency in transportation.
- What advantages does this dataset offer in improving traffic management efficiency?
- By automatically recognizing traffic signs in tunnels, it can reduce human errors, manage traffic conditions in real-time, and enhance response to emergencies.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Tunnel Traffic Control Sign Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/5030d3cb1f6305eb1c77ece8d9e3ffbc},
urldate={2025-09-15},
keywords={Tunnel Traffic Dataset, Traffic Control Sign Recognition, Target Detection Dataset},
version={1.0}
}Using this in research? Please cite us.
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