Title:	Sim to Real: Automatic manufacturing quality inspection based on computer vision
DNr:	Berzelius-2022-131
Project Type:	LiU Berzelius
Principal Investigator:	Atsuto Maki <atsuto@kth.se>
Affiliation:	Kungliga Tekniska högskolan
Duration:	2022-06-30 – 2023-01-01
Classification:	10207
Keywords:

Abstract

A challenge to applying deep learning-based computer vision technologies for manufacturing quality inspection lies in the cost, time, and manual effect of collecting a large amount of annotated training data. This project aims to solve this challenge by training a model with only synthetics data generated from manufacturing CAD models. The model should be about to ignore the gap between the simulation and real data, which means it will only be trained with synthetics data, but be tested on real data and achieve promising results. This project is related to Scania CV AB and KTH, and partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Marianne and Marcus Wallenberg Foundation.