A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

Abstract

In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for general image recognition tasks, application in industry is often precluded for three reasons: 1) large pre-trained DNNs are built on hundreds of millions of parameters, making deployment on many devices impossible, 2) the underlying dataset for pre-training consists of general objects, while industrial cases often consist of very specific objects, such as structures on solar wafers, 3) potentially biased pre-trained DNNs raise legal issues for companies. As a remedy, we study neural networks for CV that we train from scratch. For this purpose, we use a real-world case from a solar wafer manufacturer. We find that our neural networks achieve similar performances as pre-trained DNNs, even though they consist of far fewer parameters and do not rely on third-party datasets.

Automated segmentation of the healed anterior cruciate ligament from T2* relaxometry MRI scans

“…Both transfer learning and image augmentation significantly reduce the quantity of data needed to train a deep learning model. 35,36 The model may also be retrained and updated as more data become available. In addition, it is possible that the model performed as well as the retest segmentation by the ground truth segmenter, because it was overfitting that particular segmenter, who was the source of the T 2 * training data.…”