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file_name
stringclasses
5 values
quality
stringclasses
5 values
crack_presence
stringclasses
1 value
crack_location
stringclasses
5 values
crack_length
stringclasses
4 values
crack_width
stringclasses
5 values
crack_depth
stringclasses
2 values
crack_type
stringclasses
3 values
image_quality
stringclasses
1 value
lighting_conditions
stringclasses
3 values
panel_color
stringclasses
1 value
background_clarity
stringclasses
2 values
29e38c4c563d11cabb6879a292ba1c80.jpg
1080*1386
Present
Upper right corner
Approximately 300 millimeters
Approximately 1 millimeter
Unknown
Surface crack
Clear
Natural light
Black
Clear
4f8e30989a50a611d6e19101a8e3bc1d.jpg
1080*1411
Present
Bottom Right
100 mm
1 mm
Uncertain
Through Crack
Clear
Artificial Light
Black
Clear
53eaa464d0ed875b09d47d0786ecd4dd.jpg
1080*1408
Present
Multiple locations, Center and Edge
Varying lengths of multiple cracks
Varying widths of multiple cracks
Unknown
Through Crack
Clear
Natural Light
Black
Clear
8babceee8b78589b8fcc56abe39c4dc2.jpg
1080*1440
Present
Left Side
100 mm
2 mm
Unknown
Surface Crack
Clear
Natural Light
Black
Clear
c0bd35749118e3b805691338339d79b7.jpg
1080*1892
Present
Center Right
Indeterminate, extending in multiple directions
Thin
Unknown
Through Crack
Clear
Natural Light
Black
Blurred

Induction Cooker Ceramic Panel Crack Identification Dataset

In the current industrial field, the crack problem of induction cooker ceramic panels poses a threat to product safety, leading to potential explosion risks. Existing detection methods mostly rely on manual inspection, which is inefficient and prone to errors. This dataset aims to provide high-quality crack image data to train machine learning models, automating the detection process and improving detection efficiency and accuracy. The dataset contains 5000 crack images taken with high-resolution cameras in actual production environments, ensuring the data is authentic and reliable. Quality control measures include multiple rounds of annotation, consistency checks, and expert reviews to ensure the accuracy of the annotations. Data is stored in JPG format for easy loading and processing.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
crack_presence boolean Determines the presence of cracks on the ceramic panel as a boolean value.
crack_location string The specific location of cracks on the ceramic panel, such as the upper left corner, lower right corner, etc.
crack_length float The length of the crack, measured in millimeters.
crack_width float The width of the crack, measured in millimeters.
crack_depth float The depth of the crack, measured in millimeters.
crack_type string The type of crack, such as surface crack, through crack, etc.
image_quality string The quality rating of the image, such as clear, blurred, etc.
lighting_conditions string Description of lighting conditions when the image was taken, such as natural light, artificial light, etc.
panel_color string The color of the ceramic panel of the induction cooker.
background_clarity string Description of the clarity of the background, such as clear, blurred, etc.

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

Source & Contact

If you need more dataset details, please visit Mobiusi. or contact us via contact@mobiusi.com

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