Controlled Synthesis of Fibre-reinforced Plastics Images from Segmentation Maps using Generative Adversarial Neural Networks

The replacement of traditional construction materials with lightweight fibre-reinforced plastics is an accepted way to reduce emissions. By automating quality assurance, errors in production can be detected earlier, avoiding follow-up costs and hard-to-recycle scrap. Deep learning based defect detection systems have shown promising results, but their prediction accuracy often suffers from scarce labelled data in production processes. Especially in the domain of fibre-reinforced plastics, the task remains challenging because of varying textile specific errors. In our work, we applied conditional generative adversarial networks combined with image-to-image translation methods to address data scarcity through generating synthetic images. By training a generative model on image-segmentation pairs, we produce realistic fibre images matching the given segmentation maps. Our model enables control over generated output images of arbitrary fibre shapes and structures, including gaps, ondulations, and folds as error classes. We evaluate our synthetic images based on GAN metrics, feature distribution and show that they are suitable as a data augmentation method to improve the error classification performance of deep neural networks. Thereby, we provide a solution for the manufacturing domain of fibre-reinforced plastics with scarce data, consequently contributing to an automated defect detection system that reduces resource-intensive scrap in the future.