Brackets and Hangers Object Detection Dataset
#Object Detection #Image Classification #Equipment Inspection #Assembly Quality Control
- 20000 records
- 3.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-03-14
AI Analysis & Value Prop
The current industrial sector faces challenges in the accurate inspection of equipment assembly, especially for electrical cabinets and related components. Existing solutions often lack precision and require extensive manual intervention, leading to delays and increased operational costs. This dataset aims to address the technical issue of object detection in assembly processes, providing a robust solution to enhance automation and efficiency in quality control. The data was collected using high-resolution cameras in controlled environments, ensuring clarity and detail in the images. Quality control measures included multiple rounds of annotation, consistency checks, and expert reviews to ensure high-quality data. The dataset is organized in a structured format with clear labeling to facilitate easy access and processing.
Dataset Insights
Sample Examples
0a198209**.jpg|1500*2000|882.08 KB
76163fc3**.jpg|1279*1712|693.72 KB
0f83c633**.jpg|1707*1279|592.34 KB
33c0ea63**.jpg|1707*1279|620.44 KB
a59c19ce**.jpg|1280*1280|643.33 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_type | string | The type of bracket or hanger in the image. |
| object_position | json | The location coordinates of the target in the image (usually bounding box coordinates). |
| object_count | int | The number of targets identified in the image. |
| illumination_condition | string | The lighting conditions at the time the image was taken. |
| image_quality | string | The clarity and resolution of the image. |
| background_complexity | string | The complexity level of the background, including simple, complex, or no background. |
| object_material | string | The material of the bracket or hanger, such as metal, plastic, etc. |
| capture_angle | string | The perspective of the image when taken, such as front view, side view, top view, etc. |
| object_color | string | The color of the bracket or pendant. |
| object_size | string | The size or dimensions information of the bracket or pendant. |
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 images are included in the Brackets and Hangers Object Detection Dataset?
- The dataset includes images of electrical cabinets and equipment frame parts.
- How can the Brackets and Hangers Object Detection Dataset improve industrial automation?
- By training detection models to recognize industrial parts, it enhances the accuracy of automated equipment and reduces errors.
- How does the dataset assist in quality control?
- By accurately detecting defects in electrical components, it helps identify and eliminate issues, ensuring quality.
- Who is suitable to use the Brackets and Hangers Object Detection Dataset?
- This dataset is suitable for researchers and engineers focusing on industrial automation and quality control.
- In which industry sector is the Brackets and Hangers Object Detection Dataset most widely used?
- This dataset is widely used in the industrial sector, particularly in industries involving electrical components.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Brackets and Hangers Object Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/161da4ec14ae75f0dcb7951df2305c7e?dataset_scene_id=2},
urldate={2025-08-28},
keywords={object detection dataset,industrial inspection,assembly quality control,image dataset},
version={1.0}
}Using this in research? Please cite us.
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