# Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection
###### 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. deep learning; data augmentation; quality control; industry 4.0; structural adhesive +
Footnote †: journal:and relatively low frequency of these tests make it so that data availability remains a tremendous challenge for the industrial viability and adoption of such solutions. Even in cases for which defect data can be made available, it is difficult to ensure that a balanced number of samples of each defect type is included. Consequently, one of the main barriers in modern deep learning-based approaches concerning quality control is the vast amount of training data necessary to develop these solutions. Large datasets require considerable human effort to generate from scratch, whilst generally being costly, time-consuming and error-prone. To mitigate this issue, data augmentation with synthetic data is emerging as a possible solution to decrease the burden of data collection and annotation [5]. Recently, a systematic review [6] of the current Industrial AI landscape has shown that the usage of synthetic data with the purpose of artificially augmenting datasets in industrial applications was present in around 20% of publications included in the study. With the important role that realistic and high-resolution synthetic images can play in industrial computer vision tasks, Generative Adversarial Networks (GANs) [7] are becoming more and more attractive as a way to reliably generate additional samples in the manufacturing domain. While generative methods have been prevalent in recent literature as a way to generate novel samples from high-dimensional data distributions (for instance in the case of images), previous methods such as those based on variational autoencoders [8] tended to produce blurry results due to restrictions in the model. GANs are generally capable of producing sharper images [9] with recent advances making it possible to generate synthetic samples with increasingly large levels of quality [10; 11]. Some examples of GANs in the manufacturing domain can be found in the literature, addressing applications in which the data are scarce and difficult to obtain [12; 13; 14]. Other relevant examples include efforts addressing cases in which labeling the entire dataset may be unfeasible, with semi-supervised learning methods showing promise in this direction [15; 16; 17].