# End-to-End Defect Detection in Automated Fiber Placement Based on Artificially Generated Data

arXiv: https://arxiv.org/abs/1910.04997 · HTML: https://mlsanity.com/paper/1910.04997/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 4 of 4)

- [Synthetic training data generation for deep learning based quality inspection](https://mlsanity.com/paper/2104.02980/) (2021)
- [Data refinement for fully unsupervised visual inspection using pre-trained networks](https://mlsanity.com/paper/2202.12759/) (2022)
- [Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection](https://mlsanity.com/paper/2106.01277/) (2021)
- [Improving generalization with synthetic training data for deep learning based quality inspection](https://mlsanity.com/paper/2202.12818/) (2022)

## Abstract (arXiv)

Automated fiber placement (AFP) is an advanced manufacturing technology that increases the rate of production of composite materials. At the same time, the need for adaptable and fast inline control methods of such parts raises. Existing inspection systems make use of handcrafted filter chains and feature detectors, tuned for a specific measurement methods by domain experts. These methods hardly scale to new defects or different measurement devices. In this paper, we propose to formulate AFP defect detection as an image segmentation problem that can be solved in an end-to-end fashion using artificially generated training data. We employ a probabilistic graphical model to generate training images and annotations. We then train a deep neural network based on recent architectures designed for image segmentation. This leads to an appealing method that scales well with new defect types and measurement devices and requires little real world data for training.