Smartphone-Based Digital Image Processing for Fabric Drape Assessment

Fabric drape characterization is vital for textile performance and aesthetics, but the conventional Cusick method is labor-intensive and incompatible with digital workflows. This study assesses a smartphone-enabled digital image processing (DIP) method for fabric drape coefficient (DC) measurement, providing an accessible, low-cost alternative to the Cusick method. Draped specimens of light, medium, and heavy fabrics were imaged at three diameters (24, 30, and 36 cm) using a smartphone positioned at three vertical distances (22, 32, and 42 cm). DCs were determined through pixel-based analysis in Adobe Photoshop®, ImageJ®, and MATLAB®. Statistical comparison against the Cusick method employed F-tests for variance, two-sample t-tests for mean differences, and skewness analysis. No statistically significant differences were found between smartphone DIP (with either the iPhone or Samsung device) and Cusick measurements (p > 0.05). Neither imaging height nor software platform significantly influenced outcomes, though a 22 cm height consistently provided the most stable conditions. ImageJ® was identified as an effective open-source option for reliable analysis. The findings establish a reliable, cost-effective, and portable method for drape evaluation, reducing technical and economic barriers while aligning with Industry 4.0 digitalization. This approach enables broader adoption of reliable textile characterization across research, industry, and domains.