Air Conditioning Copper Pipe Joint Solder Residue Detection Dataset
#Object Detection #Image Classification #Anomaly Detection #Industrial Inspection #Quality Control #Automated Manufacturing
- 5000 records
- 1.2G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The current industrial sector faces significant challenges with ensuring the quality of solder joints in air conditioning copper pipe assemblies. Residual solder can lead to blockages, affecting the efficiency and safety of the systems. Existing solutions often rely on manual inspection, which is time-consuming and prone to human error. This dataset aims to address the need for automated inspection systems that can accurately detect solder residue. The dataset is structured to include diverse images captured under various lighting conditions and angles, ensuring comprehensive coverage. Data collection involved using high-resolution cameras in a controlled manufacturing environment. Quality control included multi-stage annotations by trained personnel, consistency checks, and expert review to ensure high accuracy. The images are stored in JPEG format and organized systematically for easy access and analysis.
Dataset Insights
Sample Examples
7cb4e302**.png|1983*1500|2.54 MB
fda971cf**.png|2052*1500|2.41 MB
25466875**.png|2193*1500|2.31 MB
e551ec36**.png|1476*1500|2.45 MB
06e19345**.png|2049*1500|2.18 MB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| solder_presence | boolean | The presence of solder residue. |
| solder_location | string | The relative position of the solder residue in the image. |
| surface_condition | string | The condition of the copper pipe surface, such as whether oxidation is present. |
| joint_quality | string | Evaluation of the quality of welded joints, such as 'Good' or 'Average'. |
| defect_severity | string | Severity level of welding defects, such as 'Minor' or 'Severe'. |
| angle_of_view | float | Shooting angle used to analyze the detection accuracy. |
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 is the Air Conditioner Copper Pipe Joint Solder Residue Detection Dataset?
- The Air Conditioner Copper Pipe Joint Solder Residue Detection Dataset is used to detect solder residue on the welding parts of air conditioner copper pipes to enhance efficiency and accuracy in quality monitoring.
- What is the main application of this dataset?
- The dataset is mainly used to enhance the efficiency and accuracy of monitoring the quality of air conditioner copper pipe welding, aiding in more precise quality control in the industrial sector.
- How does the dataset help improve welding quality?
- By detecting solder residue on air conditioner copper pipe joints, the dataset helps identify and reduce excess materials that may occur during welding, thus improving welding quality.
- What data modality is used in this dataset?
- The dataset uses image modality, suitable for object detection tasks.
- How does the dataset promote quality monitoring in the industrial sector?
- By automating the detection of solder residue on air conditioner copper pipe welds, the dataset reduces human error and enhances detection efficiency, thereby improving quality control processes in the industrial sector.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Air Conditioning Copper Pipe Joint Solder Residue Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/17612898d1c404ee46cec8e8dfff967e?dataset_scene_id=2},
urldate={2025-08-28},
keywords={air conditioning,solder residue detection,industrial dataset,image processing,quality control},
version={1.0}
}Using this in research? Please cite us.
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