Deep learning in the automotive industry: Applications and tools
Open accessAbstract
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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Deep learning-based approaches show great advantages in surface defect detection and other industrial applications.
“deep learning-based approaches show great advantages in both surface defects detection and other industrial applications, such as the automotive industry [2], fruit classification [3], and object detection [4,5].”
From Deep Metallic Surface Defect Detection: The New Benchmark and Detection Network (2020) · cites this paper as [2]
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- Authors
- Luckow, A., Cook, M., Ashcraft, N., Weill, E., Djerekarov, E., & Vorster, B.
- Affiliations
- Clemson University (US)
- University of Cincinnati (US)
- Venue
- 2016 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 3759-3768
- OpenAlex
- W2583832915
- Cited by
- 164
- References
- 49
- Citations by year
- 2026: 2 · 2025: 12 · 2024: 17 · 2023: 15 · 2022: 26 · 2021: 29 · 2020: 24 · 2019: 16 · 2018: 16 · 2017: 3 · 2015: 2 · 2013: 1 · 2012: 1
- FWCI
- 4.05
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Keywords
- convolutional neural networks, computer vision, visual inspection, manufacturing quality control, annotated dataset, GPU training, neural network framework
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