The use of Deep Learning in the automotive sector


Deep Learning the automotive sector is used increasingly like an automated learning method. It allows you to process data from your systems of computer vision, pattern recognition and sensors to reach bigger quality quotes and safety during manufacturing.

What is Deep Learning and how does it work?

Deep Learning is a kind of automated learning that uses artificial neuronal networks to learn from data. Neuronal networks are mathematics models inspired by the human brain. Each artificial neuron receives information from neurons of the anterior layer and processes that information to generate an exit that is sent to the neurons in the next layer.

Deep Learning works throughout processes of supervised and no supervised learning. In the case of the supervised learning algorithms, neuronal networks feed on labelled data, which means that data already have a correct answer. Neuronal networks learn to make the correspondent task as more labelled data are provided to them.

For example, if we want to train a neuronal network to recognize failures in a piece, we can feed it with a set of data from images of pieces with failure and without failures. Each image from the data set must be labelled with the corresponding class, which means, the piece “ok” or piece “nok”. As the neuronal network processes the images from the data set, it will learn to identify the patters that distinguish the pieces that have any defect from the pieces that do not present any defect.

How to apply Deep Learning in the automotive industry?

To apply Deep Learning in automotive sector, we recommend to follow the next steps:

  1. Define the objective of the application. What do you want to achieve with Deep Learning? Do you want to improve the safety, efficiency or comfort of vehicles?
  2. Gather the necessary data. Data can be of different types, such as images, videos, audio or data from sensors. The kind of data that you need will depend on the objective of the application.
  3. Labelling the data. Labelled data are those that have associated values or categories. The labelled is necessary for the algorithm of the Deep Learning can learn from the data.
  4. Train the algorithm. The Deep learning algorithm will be trained with the labelled data. The training can last several hours or even days, depending on the complexity of the algorithm and the size of the set of data.
  5. Evaluate the algorithm. Once the algorithm has been trained, it is necessary to evaluate its performance through different metrics. For this, it will be used a test data set that has not been used for the training.

Benefits of Deep Learning in automotive

The use of Deep Learning in the automotive sector has numerous benefits, we show you some of them:

  • Improving safety: Deep Learning can be used to develop systems of autonomous driving and advanced driving assistance systems, which can help to prevent accidents. For instance, ADAS systems based on Deep Learning can detect pedestrians, cyclists and other vehicles in the road, and alert the driver to the existence of a danger.
  • Greater efficiency: Deep learning can be used to improve the efficiency of vehicles and its manufacturing processes. For example, cruise control systems based on Deep learning help drivers save fuel by maintaining a constant speed while driving.
  • Greater comfort: Improving the comfort of vehicles can also be carried out with Deep learning. Among others, navigation systems can provide more precise directions.

Computer vision and Deep learning applications in automotive

In this section we explain some applications in the automotive world in which Deep Learning is used. These three applications are about improvements in automobile production.

  • Detection of defects in the assembly line: in the world of the automotive industry, production costs are high, and in order to have competitive prices in the market, companies seek to reduce possible losses as much as possible. To this end, computer vision systems and Deep learning algorithms are being implemented, with the aim of finding probable defects at different points in the assembly chain, and thus being able to solve them before the production of these vehicles advances and the cost of resolve it later is higher.

  • Correct placement of parts on assembly lines: in this case, Deep learning is used to check if the parts that have been unloaded by a person or a robot at one of the workstations are in the correct position , since, if not, the next steps in the production line will be affected and will delay the work, causing production costs to increase.
  • Reading the identification labels carried by vehicles: to carry out this described task, the optical character recognition (OCR) technique will be used. This technology uses image processing and character classification algorithms, and the use of Deep learning will help us improve OCR technology. These two techniques will allow factories to have exact control of all the vehicles produced in their company, knowing at all times possible defects or failures that have been recorded throughout the production chain.

On the other hand, we also explain three applications related to the car itself:

  • Autonomous driving: Deep Learning is used so that vehicles can detect obstacles and other vehicles on the road, as well as to control the speed and direction of the vehicle.
  • Detection of pedestrians and cyclists: Deep Learning is also used so that vehicles can detect pedestrians and cyclists on the road, thus avoiding accidents.
  • Predictive maintenance: Deep Learning is used to analyze data from vehicle sensors, and as a consequence detect possible problems before they occur.

Deep learning is a technology that has the potential to revolutionize the automotive sector. By enabling vehicles to make smart and safe decisions, deep learning can help reduce the number of traffic accidents and improve vehicle comfort and convenience.

In the coming years, Deep learning is likely to be increasingly used in the automotive sector. As technology continues to develop, new applications can be created that will further improve vehicle safety, comfort and efficiency.

Do you want to apply Deep Learning in any of your automotive projects? Contact us!



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