Night Road Multi-Target Recognition Image Dataset
#object detection #image recognition #intelligent transportation #autonomous driving #security monitoring
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The current transportation industry faces safety risks during night driving, especially under low light conditions, where the recognition accuracy of vehicles, traffic police, and environmental targets is lower, leading to frequent traffic accidents. Existing object detection models often perform poorly at night scenes due to a lack of sufficient training data. This dataset aims to improve the performance of object detection algorithms under complex lighting conditions by providing a rich dataset of night-time road images. The dataset includes 5000 images taken at night, covering various traffic scenarios. Data collection was done using high-sensitivity cameras in urban road environments to ensure the diversity and authenticity of the data. Quality control involves expert reviews and multiple rounds of annotation to ensure consistency and accuracy. The data storage format is JPG, organized by category and timestamp.
Dataset Insights
Sample Examples
eda7eca7**.jpg|1707*1280|334.38 KB
91870204**.jpg|3000*4000|1.05 MB
6b6b534b**.jpg|1440*1920|258.09 KB
270b71ab**.jpg|2560*1702|422.63 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| vehicle_count | int | The total number of vehicles identified in the image. |
| police_presence | boolean | Indicates whether there is a police presence in the image. |
| road_condition | string | A description of the road conditions observed in the image, such as 'dry' or 'slippery'. |
| lighting_condition | string | A description of the lighting conditions in the image, such as 'well-lit' or 'poor lighting'. |
| weather_condition | string | A description of the weather conditions in the image, such as 'clear' or 'rainy'. |
| environment_visibility | int | The visibility level of the environment on a scale from 0 to 10, with 10 being the highest visibility. |
| object_distance | float | The estimated distance of the nearest object in the image to the camera, measured in meters. |
| vehicle_type_distribution | json | The distribution of vehicle types in the image, such as cars and trucks. |
| pedestrian_count | int | The total number of pedestrians identified in the image. |
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 transportation applications is this dataset suitable for?
- This dataset is suitable for nighttime traffic monitoring, autonomous driving assistance systems, and traffic safety research.
- What is the quality of the images in the dataset?
- The images in the dataset are carefully collected and processed to ensure good recognition performance in nighttime environments.
- Does the dataset include annotations for multiple target categories?
- Yes, the dataset includes detailed annotations for multiple target categories such as vehicles, traffic police, and environment.
- How does this dataset enhance traffic safety research?
- By providing rich nighttime traffic scene data, this dataset supports more accurate target recognition and analysis, thus providing a solid data foundation for traffic safety research.
- Do the images in the dataset cover various weather conditions?
- The dataset primarily focuses on nighttime scenes but may include images captured under various nighttime weather conditions.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Night Road Multi-Target Recognition Image Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/de31df8435d3ca7a3d1e7afa9b36e6f1},
urldate={2025-09-15},
keywords={night target detection, traffic safety dataset, vehicle recognition, traffic police recognition, environmental monitoring},
version={1.0}
}Using this in research? Please cite us.
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