Air Conditioning Outlet Guiding Blade Detection Dataset
#Object Detection #Image Classification #Industrial Inspection #Quality Control #Manufacturing
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-10-25
AI Analysis & Value Prop
The air conditioning industry faces challenges in ensuring the correct installation and integrity of guiding blades, which are crucial for controlling airflow direction. Current solutions often rely on manual inspections, which can be inconsistent and error-prone. This dataset aims to address the need for automated inspection systems that can accurately detect the position and integrity of guiding blades, thereby improving quality control in manufacturing processes. Data collection involved capturing images of guiding blades under various lighting conditions using high-resolution cameras in a controlled factory environment. Quality control measures included multi-round annotations, consistency checks among different annotators, and expert reviews to ensure high precision. The dataset is organized in JPG format, with each image labeled according to the defined fields.
Dataset Insights
Sample Examples
3b11221d**.jpg|1366*911|347.77 KB
f5ec2e89**.jpg|1080*1440|158.41 KB
a71b682d**.jpg|1080*1439|18.28 KB
665fe8bf**.jpg|1080*1410|228.12 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| object_count | int | The total number of directional vanes on the air conditioner outlet in the image. |
| object_position | string | Describes the position coordinates of each directional vane in the image. |
| object_orientation | string | Direction of the air conditioning outlet vanes (horizontal, vertical, etc.). |
| object_integrity | string | The integrity status of the vanes (intact, damaged, incomplete). |
| object_type | string | The type of directional vanes on an air conditioner outlet, such as blade shape or design type. |
| object_color | string | Color recognition of the directional vanes on the air conditioner outlet. |
| object_shape | string | Detailed description of the vanes' specific shape (rectangular, oval, etc.). |
| damage_detection | boolean | Detects whether there is damage to the vanes (yes/no). |
| installation_status | string | The installation status of the vanes (correctly installed, loosely installed, not installed). |
| reflection_intensity | float | Measurement of the intensity of light reflected from the surface of the guide vane. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| 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 does the Air Conditioner Outlet Air Deflector Detection Dataset mainly include?
- This dataset mainly includes image data of the installation status and integrity of air conditioner outlet air deflectors, suitable for maintenance inspection of industrial equipment.
- Why is the Air Conditioner Outlet Air Deflector Detection Dataset important for industry?
- This dataset can help companies quickly detect installation and integrity issues of air deflectors, thereby improving equipment maintenance efficiency and reducing maintenance costs.
- Is the Air Conditioner Outlet Air Deflector Detection Dataset suitable for the development of automated detection systems?
- Yes, this dataset is suitable for developing automated detection systems and can be used to train machine learning models to detect the status of air deflectors.
- What problems can be solved using the Air Conditioner Outlet Air Deflector Detection Dataset?
- Using this dataset, it is possible to effectively detect whether the air deflector is installed correctly and completely, helping companies prevent potential equipment failures and improve maintenance predictability.
- How to use the Air Conditioner Outlet Air Deflector Detection Dataset to improve maintenance efficiency?
- By training machine learning models, this dataset allows for automated detection of air deflectors, thus speeding up maintenance and improving accuracy.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Air Conditioning Outlet Guiding Blade Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/161546020648739b7d93961875ed2070},
urldate={2025-08-28},
keywords={air conditioning,guiding blade detection,industrial inspection dataset,quality control,automated inspection},
version={1.0}
}Using this in research? Please cite us.
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