Fabric defect detection using OpenCV

Computer Science and Engineering

In the realm of quality control, the detection of defects in fabrics stands as a vital imperative to ensure product integrity and customer satisfaction. The present study addresses the limitations of existing technology in fabric defect detection and endeavors to enhance accuracy and efficiency through the integration of OpenCV and Streamlit frameworks. This research is driven by the exigency to overcome the deficiencies of conventional defect detection methods, which are often time-consuming and susceptible to human errors. The goal of this study is to devise an automated system that can effectively and accurately identify fabric defects, streamlining the quality assurance process. By leveraging the capabilities of OpenCV, a robust computer vision library, and the interactive user interface of Streamlit, a powerful web application framework, the proposed methodology orchestrates a dynamic amalgamation of image preprocessing, feature extraction, and user-friendly interaction. The study utilizes a diverse dataset of fabric samples, annotated with defect labels, to train and fine-tune the defect detection algorithm. The fusion of OpenCV's feature extraction algorithms with the efficiency of Streamlit's interface results in a real-time defect detection system. The system allows users to upload fabric images, view defect detection outcomes, and even delve into extracted features for deeper insights. The results demonstrate that the integration of OpenCV and Streamlit significantly enhances defect detection accuracy and expedites the quality control process. The developed system, through meticulous interpretation of the results, showcases its proficiency in distinguishing between normal and defective fabric regions. This research work underscores the potential of OpenCV and Streamlit integration for efficient and accurate fabric defect detection. By effectively addressing the limitations of existing techniques, this study contributes to a more reliable and expedient approach to quality assurance in the textile industry.

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