Air Conditioner Inlet Filter Blockage Image Recognition Dataset
#Image recognition #classification #deep learning #Industrial inspection #equipment maintenance #fault prediction
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-11-04
AI Analysis & Value Prop
In the current industrial field, predicting maintenance cycles of air conditioning equipment is an important issue, especially in situations of filter blockage, which can easily lead to reduced efficiency or even equipment failure. However, existing monitoring solutions heavily rely on manual inspection, which is inefficient and prone to errors. This dataset aims to automatically recognize the dust accumulation status of filters through image recognition technology to predict maintenance needs in advance. The data is collected using high-resolution images from multiple industrial environments to ensure coverage of different dust conditions. Quality control involves multiple rounds of annotation and expert review to ensure data consistency and accuracy. The data is stored in JPEG format, categorized by time and dust level for easy subsequent processing and analysis.
Dataset Insights
Sample Examples
f594fe87**.jpg|1080*1416|156.60 KB
55b81acf**.jpg|1078*1414|315.82 KB
b3678a6c**.jpg|1080*1397|287.67 KB
f1a24d80**.jpg|1080*1406|249.05 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| dust_level | integer | Indicates the degree of dust accumulation on the filter, which can be used to determine whether cleaning is needed. |
| filter_type | string | Identifies the type of filter, such as metal filter, fiber filter, etc. |
| clogging_status | string | Detects whether the filter is blocked, such as not blocked, slightly blocked, seriously blocked, etc. |
| detection_confidence | float | Represents the confidence in the filter detection results, with values ranging from 0 to 1. |
| object_location | string | Describes the location of the filter in the image, which may include coordinate information. |
| image_quality | string | Evaluation of the image quality, including clarity, blurriness, etc. |
| illumination_level | string | Detects the lighting conditions in the image, such as adequate lighting, insufficient lighting, etc. |
| filter_orientation | string | The orientation of the filter in the image, such as vertical, horizontal, etc. |
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 industrial applications can this dataset be used for?
- This air conditioner intake filter clogging image recognition dataset can be used to predict filter maintenance cycles and optimize maintenance costs.
- What is special about the air conditioner intake filter image recognition dataset?
- This dataset focuses on recognizing dust accumulation on air conditioner filters, helping to improve maintenance efficiency.
- How to use this dataset to improve prediction accuracy?
- Using deep learning models to train on this dataset can improve accuracy in predicting air conditioner filter conditions.
- Are there any limitations to using this dataset?
- The dataset is mainly suitable for industrial scenarios; applications should ensure their environment matches the data collection conditions.
- Can this dataset help understand the overall health of an air conditioning system?
- While the dataset focuses on filter status, it can be combined with other system data to assess overall air conditioning system health.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Air Conditioner Inlet Filter Blockage Image Recognition Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/c4398b22a70089db672436ea53e35ecb},
urldate={2025-09-15},
keywords={air conditioner filter, image recognition, industrial maintenance, fault prediction, dust detection},
version={1.0}
}Using this in research? Please cite us.
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