Smart Textile Check Identifying Fabric Defects and Quality Analysis

This research paper introduces an automated system for fabric defect detection, crucial for maintaining quality in the textile industry. Traditional methods of inspection are often manual, time-consuming, and prone to human error, leading to inconsistent quality control. To address these challenges, we have developed a deep learning-based approach utilizing a Convolutional Neural Network (CNN) to classify various fabric defects. The system takes an image of fabric as input, processes it using a pre-trained MobileNetV2 architecture fine-tuned on a custom dataset of fabric images, and outputs the identified defect type along with a confidence score. To enhance interpretability and provide visual evidence of the model's decision-making process, we have integrated Grad-CAM (Gradient-weighted Class Activation Mapping) to generate heatmaps highlighting the regions of interest in the fabric image that led to the defect classification. Furthermore, the system provides a user-friendly web interface built with Flask, allowing for easy upload of fabric images and visualization of results. A quality rating based on the confidence of the prediction is also provided, along with actionable suggestions for defect mitigation.