Generative adversarial networks for data augmentation in structural adhesive inspection

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

The technological advances brought forth by the Industry 4.0 paradigm have renewed the disruptive potential of artificial intelligence in the manufacturing sector, building the data-driven era on top of concepts such as Cyber-Physical Systems and the Internet of Things. However, data availability remains a major challenge for the success of these solutions, particularly concerning those based on deep learning approaches. Specifically in the quality inspection of structural adhesive applications, found commonly in the automotive domain, defect data with sufficient variety, volume and quality is generally costly, time-consuming and inefficient to obtain, jeopardizing the viability of such approaches due to data scarcity. To mitigate this, we propose a novel approach to generate synthetic training data for this application, leveraging recent breakthroughs in training generative adversarial networks with limited data to improve the performance of automated inspection methods based on deep learning, especially for imbalanced datasets. Preliminary results in a real automotive pilot cell show promise in this direction, with the approach being able to generate realistic adhesive bead images and consequently object detection models showing improved mean average precision at different thresholds when trained on the augmented dataset. For reproducibility purposes, the model weights, configurations and data encompassed in this study are made publicly available.

Original languageEnglish
Article number3086
JournalApplied Sciences (Switzerland)
Volume11
Issue number7
DOIs
Publication statusPublished - 1 Apr 2021

Keywords

  • Data augmentation
  • Deep learning
  • Industry 4.0
  • Quality control
  • Structural adhesive

Fingerprint

Dive into the research topics of 'Generative adversarial networks for data augmentation in structural adhesive inspection'. Together they form a unique fingerprint.

Cite this