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file_name
stringclasses
4 values
quality
stringclasses
4 values
bucket_position
stringclasses
4 values
imbalance_severity
stringclasses
3 values
damage_present
stringclasses
2 values
anomaly_detection
stringclasses
1 value
object_count
stringclasses
3 values
lighting_conditions
stringclasses
4 values
image_quality_score
stringclasses
1 value
part_obstruction
stringclasses
4 values
angle_of_capture
stringclasses
4 values
00638dcca96ffe7ea84dbbbf2f5cc072.jpg
1080*1440
Washing machine inner tub located slightly below the center of the image
No obvious imbalance detected
No significant damage
No anomaly detected
1
Well-lit, good conditions
High
Partially obstructed by a chair leg
Slightly top-down angle
63277f8b9905b18be78d681e715aa9ee.jpg
800*1067
Washing machine inner tub placed on the floor against the wall
No significant imbalance detected
No significant damage
No anomaly detected
No relevant object detected
Even lighting, slight shadows
High
No objects obstructing the tub
Taken from an angled position
636f0e7ec5069432ef2f6f4eaf30dbbb.jpg
1080*1302
Inner tub located at the center of the image
No imbalance detected
No significant damage visible
No anomaly detected
0
Good lighting conditions
High
No visible obstruction
Vertical top-down
e29c59fbddc6b2f5710c48861e820d8e.jpg
1620*2160
Inner tub located at the very center of the image
No significant imbalance detected
No significant damage visible
No anomaly detected
0
Even, moderate lighting
High
No obstruction
Taken from directly above

Washing Machine Drum Imbalance Detection Dataset

The washing machine industry faces significant challenges related to efficiency and longevity of appliances due to imbalanced drum operation. Current solutions often lack precise detection methods, leading to increased maintenance costs and reduced machine lifespan. This dataset aims to address the specific technical challenge of detecting imbalance in washing machine drums using image analysis, fulfilling an urgent business need for manufacturers and service providers. The dataset comprises images captured using high-resolution cameras in controlled environments, ensuring optimal lighting and angle. Quality control measures include multiple rounds of labeling, consistency checks among annotators, and expert review to ensure accuracy. The data is stored in JPG format, with organized folders for easy access and retrieval.

Technical Specifications

Field Type Description
file_name string File name
quality string Resolution
bucket_position string The position of the washing machine’s inner drum in the image
imbalance_severity integer The detected severity of imbalance in the washing machine’s inner drum
damage_present boolean Whether the washing machine’s inner drum shows any apparent damage
anomaly_detection boolean Whether any anomalies related to the inner drum position were detected
object_count integer The number of objects detected in relation to the inner drum imbalance
lighting_conditions string Analysis of the lighting conditions during the image capture
image_quality_score float A comprehensive score of image sharpness and contrast.
part_obstruction boolean Whether there are other objects or components blocking the view of the inner drum.
angle_of_capture integer The angle of the camera when the image was taken.

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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