Automatic Assessment of Fabric Soil Release Appearance Using a Lightweight Tri-Semantic Injection Network with Ordinal Learning

Soil release appearance grading evaluates residual stains after standardized laundering, but visual assessment is subjective and adjacent half grades are difficult to distinguish. This study proposes a lightweight Tri-Semantic Injection Network (TSI-Net) for nine-grade assessment. From one red–green–blue (RGB) image, a fixed CIE L*a*b* (CIELAB) branch constructs a mean-background image B, a pixel-wise color-difference image D, and a stain-appearance image S. The trainable backbone progressively injects S, D, and B and predicts grades from 1.0 to 5.0 at 0.5-grade intervals. Gaussian soft targets represent the ordering of these grades. The dataset contains 325 images, and TSI-Net has 1,403,336 trainable parameters. In a 33-image evaluation, Gaussian-trained TSI-Net achieved exact-grade and within-half-grade accuracies of 87.88% and 96.97%, compared with 84.85% and 93.94% for one-hot training. Both training methods achieved 100.00% accuracy within one grade. Gaussian training therefore showed a 3.03-percentage-point advantage for each of the two stricter metrics in this comparison. This method requires no manual stain segmentation and provides a compact framework for standardized fabric appearance assessment under controlled acquisition conditions.