---
tags:
- Object detection
- image recognition
- Traffic monitoring
- smart traffic systems
- urban management
license: cc-by-nc-sa-4.0
task_categories:
- object-detection
language:
- en
pretty_name: Road Blockage and Illegal Parking Identification Dataset
size_categories:
- 1B<n<10B
---

# Road Blockage and Illegal Parking Identification Dataset

The current traffic industry faces increasingly severe issues of road blockages and illegal parking, affecting the smoothness and safety of urban traffic. Existing traffic monitoring systems often rely on manual inspections, which are inefficient and prone to omissions. This dataset aims to provide high-quality object detection data for smart traffic systems to automatically identify road blockages and illegal parking situations, thereby enhancing real-time monitoring capabilities. During the construction of the dataset, high-resolution cameras were used to shoot around the clock at major urban traffic intersections, and machine learning algorithms were employed for automatic annotation, ensuring data diversity and accuracy. Strict quality control measures were implemented, including expert reviews and multiple rounds of annotation, to ensure high quality and consistency of the data. The data is stored in JPEG format and organized according to image IDs for easy subsequent data processing and analysis.

## Technical Specifications

| Field | Type | Description |
| :---  | :---  | :--- |
| file_name | string | File name |
| quality | string | Resolution |
| blockage_type | string | Describes the type of blockage identified in the image, such as traffic jam or illegal parking. |
| vehicle_count | int | Total number of vehicles identified in the image. |
| road_segment | string | Specifies the specific road segment or location where blockage or illegal parking is identified. |
| obstruction_severity | string | Grades the severity of the road blockage situation, such as minor, moderate, or severe. |
| weather_conditions | string | Weather conditions at the time the image was captured, potentially including sunny, cloudy, or rainy. |
| daytime | string | The time of day when the image was captured, such as morning, afternoon, or evening. |
| pedestrian_count | int | Number of pedestrians identified in the image. |
| parking_signs_present | boolean | Indicates whether parking signs are present in the image. |

## Compliance Statement

<table>
  <tr>
    <td>Authorization Type</td>
    <td>CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)</td>
  </tr>
  <tr>
    <td>Commercial Use</td>
    <td>Requires exclusive subscription or authorization contract (monthly or per-invocation charging)</td>
  </tr>
  <tr>
    <td>Privacy and Anonymization</td>
    <td>No PII, no real company names, simulated scenarios follow industry standards</td>
  </tr>
  <tr>
    <td>Compliance System</td>
    <td>Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs</td>
  </tr>
</table>

## Source & Contact

If you need more dataset details, please visit [Mobiusi](https://www.mobiusi.com/datasets/9ac743e4a3919115b3cedfb92b41c785?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com