基于机器视觉的织物洗涤后尺寸稳定性评价

针对现有手工测量织物洗涤后尺寸变化方法存在误差大和精度低的问题，尤其是在洗涤后织物试样形状不规则情况下，手工测量结果难以全面准确反映织物整体尺寸变化，本文提出了基于机器视觉方法实现织物洗涤前后面积变化的精确检测，以面积变化率为指标来评价织物洗涤后尺寸稳定性。选择了6种不同规格的纯毛织物为试验材料，设置不同的洗涤、漂洗和脱水程序对试样进行洗涤，待干燥后采用机器视觉系统获取试样图像。对试样图像进行处理得出试样洗涤前后经、纬向平均长度和面积的变化率，以这些指标对试样尺寸稳定性进行评价。与人工测量方法相比，机器视觉方法可以实现对织物洗涤后尺寸变化的完全表征和准确评价，并可为织物设计和洗涤程序优化提供参考。

Evaluation method for dimensional stability of washed fabric based on computer vision

According to the existing manual methods to measure dimensional changes of fabric after laundering has an obvious error and low accuracy, especially the case with irregular shape, this paper proposes a method based on machine vision to calculate the fabric area change and then evaluate dimension stability of fabric after being washed. Six types of pure wool fabrics are chosen as experimental materials, which will be washed under different washing, rinse, spin time procedures before dry and image capture by machine vision system. The dimensional stability will be evaluated by taking the average dimensional change ratio not only in both warp and weft direction, but also in area before and after laundering, which will be calculated from the fabric images. Compared with the manual measurement, the computer vision method proposed in this paper can represent the dimensional change more globally and accurately, which provides a test basis for fabric design and washing program optimization.