Refrigerator Condenser Solder Joint Defect Detection Dataset
#Image Classification #Object Detection #Anomaly Detection #Industrial Inspection #Quality Control #Defect Detection
- 5000 records
- 1.5G
- JPG/PNG/JSON
- CATL
MOBIUSI INC
Updated:2026-09-21
AI Analysis & Value Prop
The current industrial landscape faces significant challenges in maintaining the efficiency of refrigeration systems, particularly concerning the quality of solder joints in condensers. Existing solutions often fall short in accurately detecting defects like cold solder joints and pores, which can severely impact cooling efficiency. This dataset aims to address the specific technical need for precise defect detection in solder joints, enhancing quality control processes in manufacturing. The data was collected using high-resolution cameras in controlled environments, ensuring clarity and detail for accurate inspection. Quality control measures include multi-round annotations, consistency checks among annotators, and expert reviews to ensure data reliability. The dataset is structured in JPG format, organized by unique IDs for each image and associated metadata, facilitating easy access and analysis.
Dataset Insights
Sample Examples
e3f3fa7b**.jpg|1920*1280|1.51 MB
982aeee4**.jpg|800*1422|161.96 KB
880715bd**.jpg|1100*950|73.04 KB
717de938**.jpg|1067*800|231.85 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| defect_type | string | The category of welding defects, such as cracks, incomplete welding, weld spatter, etc. |
| defect_severity | string | The severity of the defect, such as minor, moderate, severe. |
| defect_location | string | A specific description of the defect's location in the condenser. |
| bounding_box | string | Location information of the target defect's bounding box, usually the coordinates of the top-left and bottom-right corners. |
| inspection_date | date | The date of defect detection. |
| inspector_id | string | The unique identifier of the person responsible for the inspection. |
| image_quality | string | The quality assessment of the image, such as clear, blurry, etc. |
| welding_standard_compliance | string | Description of whether the solder joints meet the standard requirements. |
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 image information is included in the refrigerator condenser weld defect detection dataset?
- The dataset includes images used for detecting weld defects in refrigerator condensers, offering various angles and types of defect imagery.
- Which industrial applications' accuracy can be improved using this dataset?
- The dataset can enhance the accuracy of automated detection systems for weld defect precision in refrigerator manufacturing, thus improving product quality.
- How effective is the object detection in the refrigerator condenser weld defect detection dataset?
- The dataset significantly enhances object detection models in recognizing weld defects due to its high-quality annotations.
- Which machine learning algorithms are suitable for the refrigerator condenser weld defect dataset?
- This dataset is suitable for training and evaluating various object detection algorithms such as YOLO and Faster R-CNN to improve weld defect detection capabilities.
- How can this dataset be used to improve weld quality control?
- By training object detection models with this dataset, welding defects can be detected automatically, enhancing the efficiency and accuracy of quality control on production lines.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Refrigerator Condenser Solder Joint Defect Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/a127d323c58c3581916ffb9de23ffc2f?dataset_scene_id=2},
urldate={2025-08-28},
keywords={defect detection dataset,industrial inspection,solder joint quality,refrigerator condenser defects},
version={1.0}
}Using this in research? Please cite us.
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