An advanced machine vision technique for quality control of fabric in the textile industry

Abstract

The textile industry in Bangladesh, one of the leading sectors of the country’s economy, requires flawless product quality to ensure their worldwide reputation. However, one of the major setbacks they face is the detection of defects in fabric. Traditionally, quality control has relied on manual inspection, which is time-consuming, inefficient, and prone to human error; which incurs significant financial losses to the industry. Fabric defects introduced during manufacturing or processing make visual inspection necessary; however, the repetitive and monotonous nature of this task makes manual detection unreliable. This paper proposes a system to inspect faults on fabric in the production line of textile industries using machine vision techniques to ensure greater precision. We analyzed established deep learning based object detection models such as YOLOv8n, Single Shot MultiBox Detector (SSD) with VGG16 backbone, and Faster R-CNN with ResNet-50, and further developed a novel two-stage pipeline architecture. In our hybrid approach, YOLOv8l is employed for initial defect detection, followed by EfficientNet-B0 for classification. A custom dataset, containing a total of 9,705 images across four defect classes, was developed using pictures taken from textile factory environments to simulate real industrial settings. We also designed a prototype system for industrial automation to integrate our model into practical workflows. Fabric defect detection, automated using machine vision techniques is a growing research focus in the area, offering efficient, fast, and scalable solutions to quality control. While previous methods have been widely used, they often suffer from limitations in adaptability, accuracy, and real-world deployment. Our proposed model not only addresses these drawbacks, but also enables reliable inspection of fabric faults in industrial production. It is especially suitable for developing an economical and customizable system for fault detection. The pipeline achieved a precision of 0.842, recall of 0.819, and F1-score of 0.830—demonstrating both accuracy and industrial applicability.