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README.md

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+---
+tags:
+- Object Detection
+- Image Recognition
+- Safety Monitoring
+- Accident Prevention
+- Transportation Management
+license: cc-by-nc-sa-4.0
+task_categories:
+- object-detection
+language:
+- en
+pretty_name: Train Carriage Operational Safety Dataset
+size_categories:
+- 1B<n<10B
+---
+
+# Train Carriage Operational Safety Dataset
+
+The current transportation industry faces significant challenges in train operational safety, particularly in carriage monitoring and accident prevention. Most existing monitoring systems rely on manual inspections, which are inefficient and difficult to comprehensively cover, making it hard to eliminate safety hazards. This dataset aims to support the research and application of automated object detection technology by providing high-quality images for carriage safety monitoring, enhancing monitoring efficiency and reducing the accident rate. The dataset includes images of train carriages in operation, captured in different environments and time periods with high resolution to ensure image quality. Professional photography equipment was used during data collection, capturing in various lighting and weather conditions, and rigorous quality control measures, including multiple rounds of annotation and expert review, were taken to ensure the consistency and accuracy of data annotation. The data is stored in JPEG format, with a clear structure that is easy to use for subsequent machine learning tasks.
+
+## Technical Specifications
+
+| Field | Type | Description |
+| :---  | :---  | :--- |
+| file_name | string | File name |
+| quality | string | Resolution |
+| car_number | string | A unique identifier for each train car. |
+| object_type | string | The type of object identified in the image, such as car, obstacle, etc. |
+| bounding_box | string | Coordinates of the rectangular bounding box around the object. |
+| detection_confidence | float | Confidence score when a target is detected. |
+| weather_condition | string | Weather conditions at the time of image capture, such as sunny, rainy, etc. |
+| lighting_condition | string | Lighting level at the time of image capture, such as daytime, nighttime. |
+| damage_severity | string | The severity of the damage detected on the car, such as minor, severe. |
+| maintenance_status | string | Current maintenance status information of the car. |
+
+## 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/2a90cac2a3a28e61e4655d2db8d44571?utm_source=huggingface&utm_medium=referral). or contact us via contact@mobiusi.com