Fabric defect detection using AI and machine learning for lean and automated manufacturing of acoustic panels

Fabric defects in the conventional manufacturing of acoustic panels are detected via manual visual inspections, which are prone to problems due to human errors. Implementing an automated fabric inspection system can improve productivity and increase product quality. In this work, advanced machine learning (ML) techniques for fabric defect detection are reviewed, and two deep learning (DL) models are developed using transfer learning based on pre-trained convolutional neural network (CNN) architectures. The dataset used for this work consists of 1800 images with six different classes, made up of one class of fabric in good condition and five classes of fabric defects. The model design process involves pre-processing of the images, modification of the neural network layers, as well as selection and optimisation of the network's hyperparameters. The average accuracies of the two CNN models developed in this work, which used the GoogLeNet and the ResNet50 architectures, are 89.84% and 95.45%, respectively, showing statistically significant results. The interpretability of the models is discussed using the Grad-CAM technique. Relevant image acquisition hardware requirements are also put forward for integration with the detection software, which can enable successful deployment of the model for the automated fabric inspection.