Fabric Defect Highlighting using Machine Learning

Quality inspection is crucial in textile manufacturing, but traditional manual methods are slow, inconsistent, and prone to human error. To address these challenges, this project proposes an automated fabric defect detection system using computer vision and machine learning. The system identifies defects such as holes, stains and uneven weaves through image analysis. It leverages OpenCV for image processing and Support Vector Machines (SVMs) for defect classification. Deployment through Flask or Streamlit enables easy application access, while real-time camera integration supports immediate defect detection, enhancing the efficiency of textile quality control