Abstract The unidirectional water transport performance of textiles is a critical determinant of thermal comfort in summer and athletic apparel. Despite advancements in manufacturing technologies for unidirectional water-transporting fabrics, existing detection methods have been found to exhibit significant limitations. Conventional detection approaches (e.g., wicking, droplet absorption, and water retention tests) are limited to capturing instantaneous snapshots of liquid transfer and thus cannot dynamically monitor the complete water-penetration process. Furthermore, resistance-based methods such as the Moisture Management Tester (MMT), which tracks water diffusion through interlayer conductivity, are significantly limited when applied to conductive fabrics, ultrathin textiles, or coated materials. To address these limitations, an innovative evaluation system based on temporal image analysis was developed. A dedicated experimental platform integrating ultraviolet (UV) optical imaging modules, equipped with high-resolution charge-coupled device (CCD) cameras and precision microfluidic systems, was constructed, enabling high-frame-rate (88.25 fps) dynamic imaging of fabric wetting processes. A two-stage image processing workflow combining contrast-limited adaptive histogram equalization (CLAHE) and the distance-regularized level set evolution (DRLSE) model was developed to accurately extract dynamic boundary features of wetting regions. Eight fabric types, including single-layer, composite, and mesh-structured samples, were evaluated to represent typical textile architectures. Time-series analysis of wetting spot area evolution revealed structure-dependent water transport behaviors: mesh structures exhibited bidirectional permeability, composite layered fabrics demonstrated gradient transport patterns, and single-layer textiles displayed homogeneous liquid dispersion. The proposed dynamic image analysis method overcomes the instantaneous sampling limitations inherent to conventional approaches, offering a real-time visual framework for assessing textile moisture management.
Jin et al. (Thu,) studied this question.