# Deep Learning in the Automotive Industry: Applications and Tools

## TL;DR

This paper explores deep learning applications in the automotive industry, focusing on computer vision, tools, datasets, and an end-to-end system for vehicle property recognition, demonstrating real-world manufacturing effectiveness.

## Contribution

It introduces a new automotive dataset and an end-to-end deep learning application with mobile data collection and cloud training infrastructure.

## Key findings

- Deep learning effectively automates vehicle property recognition.
- Cloud and GPU infrastructures reduce training times and improve accuracy.
- The trained classifier performs well in real-world manufacturing settings.

## Abstract

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.