Belt Detection Dataset
#Object Detection #Path Recognition #Belt Monitoring #Industrial Quality Control
- 15000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-15
AI Analysis & Value Prop
The current industrial sector faces significant challenges in ensuring optimal belt operation, which is critical for manufacturing efficiency. Existing solutions often lack real-time monitoring capabilities and fail to provide accurate assessments of belt conditions. This dataset aims to address these technical issues by providing high-quality images for effective object detection and path recognition of belts. The data was collected using high-resolution cameras in controlled industrial environments. Quality control was ensured through multiple rounds of annotation, consistency checks, and expert reviews. Data is stored in JPG format, organized by specific conditions and labeled accurately.
Dataset Insights
Sample Examples
00b16850**.jpg|984*1813|918.89 KB
f54c88cf**.jpg|1080*726|368.23 KB
03d34c59**.jpg|984*1813|918.89 KB
2c6aa8b2**.jpg|1080*1330|882.41 KB
9b7c51fb**.jpg|1080*1317|656.91 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| image_quality | string | Quality rating of the image, such as high, medium, low. |
| image_brightness | float | Overall brightness value of the image. |
| image_contrast | float | Contrast value of the image. |
| belt_position | string | Detailed description of the belt's position in the image. |
| belt_path | string | Description of the belt's movement path. |
| slack_detection | boolean | Whether there is slack in the belt in the image. |
| defect_annotation | string | Description of belt defects in the image, such as cracks, wear, etc. |
| object_count | integer | Number of recognizable objects in the image. |
| lighting_conditions | string | Description of lighting conditions during image capture |
| focus_quality | string | Description of the image focus condition, such as sharp, blurred |
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 types of image data are included in the Timing Belt Detection Dataset?
- The dataset primarily includes high-resolution images for detecting and identifying timing belt paths and slack.
- How can the Timing Belt Detection Dataset be used for industrial applications?
- The dataset can be used to train machine learning models for automatic monitoring and maintenance of timing belts in industrial equipment.
- What are the advantages of this dataset for object detection?
- The Timing Belt Detection Dataset offers high-quality object detection annotations, which can enhance detection accuracy and efficiency.
- What role does the Timing Belt Detection Dataset play in the maintenance of industrial equipment?
- By identifying the path and slack of timing belts, it helps in preventing equipment failures and facilitating predictive maintenance.
- What technical requirements are needed to train models using the Timing Belt Detection Dataset?
- It requires knowledge of image processing techniques and machine learning algorithms, particularly the ability to apply object detection algorithms.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Belt Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/471d11c3ad05d13025e76d901528fdf5},
urldate={2025-08-28},
keywords={belt detection dataset,industrial monitoring,object detection,path recognition},
version={1.0}
}Using this in research? Please cite us.
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