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Deep Learning in the Automotive Industry: Applications and Tools

Andre Luckow, Matthew Cook, Nathan Ashcraft, Edwin Weill, Emil Djerekarov, Bennie Vorster
BMW Group, IT Research Center, Information Management Americas, Greenville, SC 29607, USA
arXiv:1705.00346 [cs.LG], (30 Apr 2017)

@article{luckow2017deep,

   title={Deep Learning in the Automotive Industry: Applications and Tools},

   author={Luckow, Andre and Cook, Matthew and Ashcraft, Nathan and Weill, Edwin and Djerekarov, Emil and Vorster, Bennie},

   year={2017},

   month={apr},

   archivePrefix={"arXiv"},

   primaryClass={cs.LG},

   doi={10.1109/BigData.2016.7841045}

}

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Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, tools and infrastructures (e.g. GPUs and clouds) for implementing, training and deploying deep neural networks. We particularly focus on convolutional neural networks and computer vision use cases, such as the visual inspection process in manufacturing plants and the analysis of social media data. To train neural networks, curated and labeled datasets are essential. In particular, both the availability and scope of such datasets is typically very limited. A main contribution of this paper is the creation of an automotive dataset, that allows us to learn and automatically recognize different vehicle properties. We describe an end-to-end deep learning application utilizing a mobile app for data collection and process support, and an Amazon-based cloud backend for storage and training. For training we evaluate the use of cloud and on-premises infrastructures (including multiple GPUs) in conjunction with different neural network architectures and frameworks. We assess both the training times as well as the accuracy of the classifier. Finally, we demonstrate the effectiveness of the trained classifier in a real world setting during manufacturing process.
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