Automated fabric inspection through convolutional neural network: an approach

• Published in: Neural computing & applications 2023-02, Vol.35 (5), p.3805-3823
• Main Authors: Thakur, Rashmi, Panghal, Deepak, Jana, Prabir, Rajan, Prasad, Ankit
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Artificial neural networks; Automation; Classification; Cloud computing; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Computer vision; Data Mining and Knowledge Discovery; Deep learning; Defects; Image Processing and Computer Vision; Inspection; Machine learning; Matlab; Neural networks; Original Article; Probability and Statistics in Computer Science; Robustness (mathematics); State-of-the-art reviews
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-022-07891-1

Automating the fabric inspection process has been attempted by several researchers in the past. However, the numerous variants of fabric, such as their construction, texture, print, and colour along with the huge gamut of fabric defects which exist, complicate the process of automating defect detection, thus making it an open challenge for researchers to come up with a one-stop, full proof robust automation solution. In the present study, an attempt has been made to detect, classify, and locate four different types of fabric defects on solid wovens, hole, knot, slub and stain, which contributes 86% of total fabric defects; using the convolutional neural network (CNN) through transfer learning on the MATLAB® platform and computer vision technology. The image dataset in this study consists of 5000 images, which is one of the largest reported till date and collected from real working environments of different apparel manufacturing units. The methodology involves four different approaches—detection approach, hierarchical approach, classification approach, and object location approach. For algorithm development, transfer learning has been adopted in which the pre-trained deep learning architectures are fine-tuned as per specific case requirement. In this study, 4 state-of-the-art deep learning models were selected as follows: VGG16, VGG19, ResNet101 and DarkNet53. This study not only focuses on defect detection and classification but also on defect location which is detailed in its fourth approach. For object location, YOLOv3 architecture was used. The defect detection, classification and location identification systems attained an accuracy of 95%, 88% and 97%, respectively. The developed model was tested on different platforms viz. Internet-based webcam and MATLAB® cloud. Detailed investigation towards challenges faced by models specific to class of defect was carried out and reported thus adding insights for probable modification required in the model. The study proposes an industry ready system for real-time implementation.