Enhanced Deep Learning for Real-Time Fabric Defect Detection in Textile Manufacturing

The system enhances the accuracy of fabric defect classification and detection based on enhanced deep learning algorithms such as CNNs like VGG16 and ResNet50. The 16-layer convolutional neural network, VGG16, is a feature extractor that captures textures, patterns, and visual characteristics in the fabric images. By overcoming the vanishing gradient problems, ResNet50 incorporates that, with a 50-layer residual architecture, helps in detecting subtle and complex defects. These models self-tune for betterment of the system, helping in minimizing training and improving precision. Integration of Faster R-CNN, therefore, makes the system apt for real-time defect identification and further processing of large numbers of images remote on-the-fly classification. An outstanding application in the textile industry, because the smallest of defects sometimes even minute are easily detected. Continuous quality control enabled and optimized production flows thereby is assured to very high quality in fabric manufacture, an effective tool in modern textile manufacturing.