Inspection of Cotton Woven Fabrics Produced by Ethiopian Textile Factories Through a Real-Time Vision-Based System

ABSTRACT

Fabric is produced by the weaving process through the interlacement of warp and weft yarn or knitting process through the loop formation of yarn. During these processes, there is a possibility of fabric defect formation which hinders the acceptability by the fabric consumers. Ethiopian textile factories practiced a human inspection system, a traditional means of detecting fabric quality, for monitoring textile fabric defects. Manual fabric defect detection helps to instantly correct small defects, but it is time-consuming and results in human error due to fatigue and lack of concentration. Moreover, the accuracy of recognizing the defect highly depends on the mental status of the person that checks the defects. This initiated the development of a better fabric defect identification system that helps textile experts to detect fabric defects with better precision and speed. This study proposes a vision-based fabric inspection system for plain woven gray fabrics with a uniform texture. Accordingly, a comprehensive Fabric Defect Detection Database (FDDD) is constructed. The fabric significant features were calculated using a convolutional neural network (CNN) which is a state-of-the-art technology in image processing and task analysis. The experimental result of this study shows an average accuracy of about 89% in fabric defect recognition.

摘要

织物是通过经纱和纬纱交织而成的织造工艺或通过纱线形成线圈而成的针织工艺生产的. 在这些过程中，有可能形成织物缺陷，这阻碍了织物消费者的可接受性. 埃塞俄比亚纺织厂采用人工检测系统，这是检测织物质量的传统方法，用于监测织物缺陷. 手动织物缺陷检测有助于立即纠正小缺陷，但由于疲劳和注意力不集中，这很耗时，还会导致人为错误. 此外，识别缺陷的准确性在很大程度上取决于检查缺陷的人的心理状态. 这启动了一个更好的织物缺陷识别系统的开发，该系统有助于纺织专家以更好的精度和速度检测织物缺陷. 本研究提出了一种基于视觉的织物检测系统，用于具有均匀纹理的平纹坯布. 因此，构建了一个全面的织物缺陷检测数据库（FDDD）. 使用卷积神经网络（CNN）计算织物的显著特征，这是图像处理和任务分析中最先进的技术. 该研究的实验结果表明，织物缺陷识别的平均准确率约为89%.

KEYWORDS:

• Image processing
• fabric defect detection
• CNN
• model evaluation
• accuracy
• inspection

关键词:

• 图像处理
• 织物缺陷检测
• CNN
• 模型评估
• 精确
• 视察

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Ethiopian Institute of Textile and Fashion Technology, Bahir Dar University.