FN-Net: A lightweight CNN-based architecture for fabric defect detection with adaptive threshold-based class determination

Anindita Suryarasmi, Chin Chun Chang, Rania Akhmalia, Maysa Marshallia, Wei Jen Wang, Deron Liang

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Deep learning technologies based on Convolution Neural Networks (CNN) have been widely used in fabric defect detection. On-site CNN model training and defect detection offer several desirable properties for the fabric manufactures, such as better data security and less connectivity requirements, when compared with the on-cloud training approach. However, computers installed at the manufacturing site are usually industrial computers with limited computing power, which are not able to run many effective CNN models. A lightweight CNN model should be used in this scenario, in order to find a balance point among defect detection, efficiency, memory consumption and model training time. This paper presents a lightweight CNN-based architecture for fabric defect detection. Compared with VGG16, MobileNetV2, EfficientNet, and DenseNet as state-of-the-art architectures, the proposed architecture, namely FN-Net, can perform training 3 to 33 times as fast as these architectures with less graphics processing unit and memory consumption. With adaptive class determination, FN-Net has an average F1 score 0.86, while VGG16 and EfficientNet as the best and the worst among the baseline models have 0.81 and 0.50, respectively.

Original languageEnglish
Article number102241
JournalDisplays
Volume73
DOIs
StatePublished - Jul 2022

Keywords

  • AOI
  • Artificial intelligence
  • Defect detection
  • Fabric manufacturing
  • Lightweight convolutional neural network

Fingerprint

Dive into the research topics of 'FN-Net: A lightweight CNN-based architecture for fabric defect detection with adaptive threshold-based class determination'. Together they form a unique fingerprint.

Cite this