Image Features for Washing Machine Test Fabric Age Grading

86.4 KB of image-derived features from fabric samples undergoing 1–100 accelerated washing cycles. Shaojin Ma published this dataset on figshare in April 2026 to investigate computer vision techniques for grading fabric degradation. Features include color, texture, and area metrics extracted from wrinkle and plain weave structure information.

Use Cases

• Training classifiers like kNN, MLP, LDA, and LR for fabric age prediction based on image features.
• Comparing the predictive performance of wrinkle-derived features versus plain weave structure-derived features.
• Validating computer vision as an objective tool for assessing base load aging in washing machine testing.
• Exploring mobile-based implementation for streamlined, sustainable appliance testing protocols.

Strengths

• Dataset includes features from fabric samples across five distinct degradation stages (C1–C5).
• LR classifier achieved accuracy scores of 0.75, 0.75, 0.62, 0.75, and 1.00 for the five stages.
• Models using plain weave structure-derived features consistently outperformed those using wrinkle-derived features.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Sample size is limited, as future work is suggested to expand it.

Provenance

Source

figshare

Collection Method

Computer vision techniques applied to images of base load fabric samples.

Freshness

Last updated 2026-04-10 17:50:00; freshness should be verified.