Artificial Intelligence and Deep Learning Applications for Automotive Manufacturing

André Luckow, Ken Kennedy, Marcin Ziolkowski, Emil Djerekarov, Matthew Cook, Edward B. Duffy, Michael Schleiss, Bennie Vorster, Edwin Weill, Ankit Kulshrestha, Melissa C. Smith

Abstract: Artificial Intelligence (AI) and Deep Learning has been steadily gaining importance due it’s potential for a broad set of science and industry applications. The success of deep learning techniques has found many applications, e.g. in the domain of computer vision and natural language understanding. Developing AI applications is a complex task with many challenges related to data collection, model training, and deployment.In this paper, we evaluate architectures, models and deployment issues related to the usage of deep learning techniques in the automotive manufacturing domain. Particularly, we focus on different computer vision problems in automotive manufacturing processes, e.g., in logistics processes. We developed several deep learning models that help to improve the quality and efficiency of these processes. Finally, we provide an analysis of the architecture, datasets and models used, and provide performance metrics for each of the different models.

External IDs:dblp:conf/bigdataconf/LuckowKZDCDSVWK18