Executive Summary:
The chip supply crisis, legal troubles, and other issues are among the difficulties the global automotive industry faces. To address these issues, business leaders are looking to various digital solutions, and computer vision is one of those answers.
Ai in the automotive industry may become smarter, safer, and more effective thanks to computer vision. Business executives invest more in this technology as they know its advantages. The greatest portion of the worldwide artificial intelligence industry, which includes computer vision, is anticipated to increase from $1.5 billion in 2021 to $7.6 billion by 2028.
To help business leaders effectively utilize this technology, this paper examines Computer Vision use cases in the automotive industry.
What exactly is computer vision?
Artificial intelligence (AI)’s field of computer vision enables computers and systems to extract useful information from digital photos, videos, and other visual data and to conduct actions or offer recommendations in response to that information. If AI gives computers the ability to think, computer vision gives them the ability to perceive, notice, and comprehend.
You require imaging or video-based datasets appropriate to the intended use case and application at the beginning of any computer vision project. To avoid feeding a CV model filthy data, tainting the results, and losing time and resources, cleaning the data is also essential to the process.
To annotate and label the photos or videos, you will need a team of annotators and, ideally, annotation tools with automation. Your ML or data science teams must train one or more CV, AI, or deep learning models after the data has been cleaned, annotated, and labeled to get a high accuracy score, eliminate bias, and produce the results you require. The model cannot move from proof of concept (POC) to production until, at that point, iterative feedback loops are created to enhance its output further.
Computer Vision System Types
2D vision systems are frequently employed in automotive vision systems for object detection and tracking tasks. They typically operate on two-dimensional images.
Manufacturing automobiles using 3D machine vision: 3D vision systems may create a three-dimensional model of the observed scene by collecting depth information. They are crucial in applications like robotic quality control and navigation.
Automotive vision system with machine learning: Advanced computer vision systems for the automobile industry use machine learning algorithms to enhance their capacity to identify and analyze complex patterns, resulting in greater decision-making and adaptability.
Top 5 Automotive Computer Vision Use Cases for 2023
Below are the top 5 Computer Vision use cases in automotive.
- Avoiding mishaps
For example, self-driving vehicles with computer vision capabilities can avoid collisions by applying autonomous braking. If the driver fails to stop the automobile, the computer vision-enabled cameras reliably identify pedestrians, vehicles, and other things.
Because of how well-designed these systems are, many nations, including the US, Australia, Japan, and the EU, have already mandated or will soon require automatic braking systems in all new vehicles.
Automobile manufacturers are attempting to make autonomous braking systems more precise and less expensive as technology advances, and the market expands.
- Automated assembly
Most mass-produced cars in the market are created in automated production facilities. Computer vision has made tremendous advancements in the industrial industry in recent years.
In the automotive industry, repetitive assembly processes are mechanized using bots and computer vision systems to reduce human error.
See how BMW scans vehicle model tags using computer vision and artificial intelligence. Business executives must be careful not to aim to replace human input with cobots when using computer vision and automation in a manufacturing facility. In the past, attempts to replace humans entirely with automated technologies have failed.
- Autonomous vehicle
The self-driving car is one of the most well-known uses of computer vision in the automobile industry. Computer vision and artificial intelligence (AI) enabled self-driving systems are revolutionizing the automobile industry, from self-driving non-commercial vehicles to heavy-duty logistics trucks.
The cars have high-definition cameras used in conjunction with a computer vision system to detect objects, people, and other vehicles, as well as the road to direct the car’s movement and teach the system how to drive the car.
Tesla is one of the well-known manufacturers of autonomous vehicles. Self-driving vehicles enhance efficiency, productivity, and safety in the logistics industry. However, the expense of self-driving systems is one difficulty. Since they need a mix of expensive cameras and sensors, they can be expensive to develop and install. To cut the cost of the systems by 90%, Toyota recently developed a powerful self-driving system that relies exclusively on inexpensive cameras, similar to the one used by Tesla.
- Driving style evaluation
Internal cameras with computer vision capabilities examine the driver’s behavior. If the systems notice anomalies or strange behavior like:
- driver oblivion
- Driver drowsiness or exhaustion
- using a mobile phone
- Low blink rate as you’re dozing off
To properly stop the car on the side of the road or to warn the driver. Accident rates could drop as a result.
- Quality Assurance
It is one of the most popular Computer Vision use cases in the automotive market. Quality control is a significant application in the automotive manufacturing sector. At the assembly line, cameras with computer vision capabilities can:
- Find welding flaws that are hidden from view in the vehicle’s frame.
- Measure the size of the car’s body sections.
- Determine tire sizes and match them to specific automobile models.
- Assemble the electronic parts, such as the battery, cooling module, electrical switches, etc., precisely.
- Look for irregularities in the painting job.
Conclusion
Therefore, computer vision is crucial to the automotive industry. Quality is sometimes disregarded in this fast-paced society as people strive for greater quantity. This technology, therefore, aids in keeping it up. It is not, however, limited to a single industry. Machine vision is widely employed in various sectors, including the automobile, healthcare, retail, and other sectors. Likewise, it has benefited them as well!
Do you also need computer vision technologies for your business? Then you’re one step closer to carrying it out. Contact us so we can develop the ideal solution for you!
FAQs:
Computer vision refers to the use of algorithms and computational techniques to enable machines to interpret and understand visual data from the environment around them. In the context of automotive, computer vision is used to enable cars to perceive their surroundings and make decisions based on that perception.
Some common computer vision use cases in automotive include:
1. Lane departure warning: Computer vision algorithms can detect the lane markings on the road and alert the driver if the car starts to drift out of its lane.
2. Pedestrian detection: Computer vision algorithms can recognize pedestrians in the car’s path and automatically apply the brakes if necessary.
3. Traffic sign recognition: Computer vision algorithms can recognize traffic signs, such as stop signs and speed limit signs, and provide visual and audio alerts to the driver.
4. Object detection: Computer vision algorithms can detect and classify objects in the car’s path, such as other cars, bicycles, and animals.
5. Driver monitoring: Computer vision algorithms can track the driver’s gaze, facial expressions, and head movements to detect signs of fatigue or distraction.
Computer vision technology is typically integrated into cars through sensors such as cameras, lidar, and radar. These sensors capture visual data from the environment, which is then processed by computer vision algorithms running on the car’s onboard computer. The processed data is then used to provide various driver assistance features, such as those mentioned above.
The benefits of using computer vision in automotive include increased safety, improved convenience, and enhanced driving experience. Computer vision enables cars to perceive their surroundings more accurately and react more quickly to potential hazards, which can help reduce accidents and save lives. Additionally, computer vision can enable new features such as autonomous driving, which can provide drivers with more convenience and a more enjoyable driving experience.
