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Computer Vision: Applications in Automotive Industry

The subject of artificial intelligence, known as computer vision, trains computers to interpret and comprehend the visual world. Robots can reliably recognize and classify objects and respond to what they “see,” Using digital images from cameras and videos and deep learning models.

In the 1950s, the first computer vision experiments were conducted, employing the first neural networks to detect the edges of an object and classify simple objects such as circles and squares. In the 1970s, the first computer vision application used optical character recognition to decipher typed or handwritten text. This innovation was utilized to translate written text for the blind.

Facial recognition apps flourished as the internet evolved in the 1990s, making massive sets of photographs available online for study. These expanding data sets made it possible for algorithms to recognize specific individuals in photographs and movies.

As the number of vehicles on the road increases, so does competition. Each manufacturer strives to develop better automobiles. Moreover, they are also concerned about quantity. Nearly 82.7 million automobiles were produced worldwide in 2021-2022. However, with the construction of so many automobiles, the likelihood of production errors has increased. So how can this problem be resolved? This is facilitated by computer vision in several of the world’s leading automobile industries.

However, what is this technology, and How does this technology benefit the industry? How can it be utilized? If you have all of these questions, simply read this article to find the answers.

Inside and outside of vehicles, such as during manufacturing, sales, and aftersales processes, Deep Learning applications have demonstrated considerable promise in the automotive industry.

How does computer vision work?

In general, computer vision technology mimics how the human brain operates. But how does our brain recognize visual objects? According to one of the prevalent hypotheses, our brains rely on patterns to decode particular items. This principle is implemented in computer vision systems.

Today’s computer vision techniques are based on pattern recognition. Massive amounts of visual data are used to train computers, process photos, label items on them, and identify patterns within them. If we send a million photographs of flowers, for instance, the computer will evaluate them, detect patterns that are common to all flowers, and then develop a model “flower.” Consequently, the computer will be able to reliably determine if an image depicts a flower whenever we send it images of flowers.

In his paper Image Processing and Computer Vision, Golan Levin provides technical information about how machines understand images. Briefly, machines interpret images as a collection of pixels, with each pixel having its own set of color values. Below is a photo of Abraham Lincoln, for example. The brightness of each pixel in this image is represented by a separate 8-bit value ranging from 0 (black) to 255 (white) (white). These numbers are recognized by software when an image is uploaded. This information is delivered as an input to the computer vision algorithm responsible for further analysis and decision-making.

Why is Computer Vision important for the Automotive Industry?

Computer Vision, Automotive Industry

Most sectors prioritize automation. This objective is intended to improve product processing and reduce manual labor. So how does machine vision help achieve this objective? This can be learned by examining the two most often functions listed below:

  • Robotic Guidance: The technology uses implanted visual sensors to locate even the tiniest 2D or 3D objects. In addition, this technique facilitates the placement of fragile goods by establishing a path. Additionally, it monitors important activities with higher precision than people. This ensures that your company’s productivity will rise without additional manual work.
  • Inspection: As stated previously, this technology can easily recognize and categorize items. Consequently, computer vision is employed in the care sector to inspect every aspect throughout production. It detects defects in every manufactured product and rejects those with defects. This covers surface detection (locating dents, scratches, etc.) and functional defects (. In addition, it entails verifying the presence or absence of car parts and examining their correct sizes and shapes. Last but not least, it continuously supervises the entire product assembly process—this aids in preserving the superior quality of every manufacturing.

The Rise of Deep Learning

To comprehend the modern process of computer vision technology, we must delve into the algorithms upon which it relies. Deep learning is a specific subset of machine learning that uses algorithms to draw insights from data. It is the foundation of modern computer vision. In contrast, machine learning relies on artificial intelligence, which serves as the foundation for both technologies (check AI design best practices to learn more about design for AI).

Deep learning is a more efficient approach to computer vision; it employs a specialized algorithm known as a neural network. It uses neural networks to extract patterns from data samples provided. The algorithms are based on the human understanding of how brains function, namely the interconnections between the cerebral cortex’s neurons.

The perceptron, a mathematical model of a biological neuron, is the fundamental unit of a neural network. Like biological neurons in the cerebral cortex, many layers of interconnected perceptron are feasible. Input values (raw data) are transferred through the perceptron network and arrive at the output layer, a prediction or highly informed estimate about a particular object. For instance, after the analysis, the machine can classify an object with X percent certainty. If you wanted to conduct facial recognition, for instance, you would need to take the following steps:

  • Create a database: You were needed to collect unique photographs of each subject you wished to track in a particular format.
  • Annotate images: Then, for each photograph, you would have to enter numerous critical data points, such as the distance between the eyes, the breadth of the bridge of the nose, the distance between the top lip and the nose, and dozens of other measurements that characterize the unique traits of each individual.
  • Capture new images: Next, it would be necessary to capture them from photography or video content. Then you had to repeat the measurement process by highlighting the image’s essential points. You also have to consider the angle at which the image was captured.

Automatic Vision System for Visual Defect Detection

Computer vision is utilized extensively in various applications in the automotive industry to enhance product quality. Most customer returns of defective products are due to cosmetic flaws, typically associated with the painting. In general, operators undertake the visual defect detection procedure. A manual examination is subjective, challenging, and time-consuming.

Automatic computer vision systems can examine the surface of manufactured components, such as wheels. Multiple cameras positioned above the production line can be used for real-time defect detection. The devices monitor the coating intensity of the wheel, looking for abnormalities such as a slight decrease in the amount of paint that would indicate a sudden problem in the painting process.

How Much Time Does It Take To Decipher An Image?

In brief, not much. This is why computer vision is so exciting: In the past, even supercomputers required days, weeks, or even months to perform all the necessary computations. However, today’s ultra-fast CPUs and related hardware and fast, dependable internet and cloud networks make the procedure lightning fast. The willingness of several of the largest businesses conducting AI research to share their work, Facebook, Google, IBM, and Microsoft, particularly by open sourcing some of their machine learning work, has been a significant contributor.

This enables others to build upon their work instead of beginning from scratch. As a result, the AI sector is thriving, and trials that once required weeks to complete may now be completed in 15 minutes. And for many real-world applications of computer vision, this process occurs continually in microseconds, allowing modern computers to be “situationally aware,” as termed by scientists.

Deep Learning in Assembly Line Part Inspection

In automotive industry applications of AI vision, deep learning has enormous potential for part inspection and fault localization. Before assembly of any vehicle, it is crucial to discover faulty produced components, such as brake components. Here, manual inspection is arduous to perform without aid.

Compared to conventional image processing, deep learning algorithms (Single Shot Detector – SSD, Faster RCNN) are more resilient in detecting many errors (Single Shot Detector – SSD, Faster Recurrent Convolutional Neural Networks). When training a deep learning system for fault identification using transfer learning on a custom-collected dataset, such methods achieved 95.6% accuracy on cylindrical grey shade brakes.

Computer Vision Technology Applications

Some individuals believe that computer vision represents the distant future of design. Not true. Computer vision is already present in numerous facets of our lives. Listed below are a few significant instances of how we currently employ this technology:

1. Automotive industry

Artificial Intelligence is creating a fundamental shift across the automobile business. As a result of the incorporation of computer vision into the grand scheme of things in 2022, the speed of life has begun to accelerate. Computer Vision technologies and implementations for 2022 will make self-driving and connected vehicles more prevalent than in 2021.

The focus of computer vision in 2022 will be transforming autonomous vehicles into intelligent visual readers, using best-in-class training data to power the algorithms and high-end annotation approaches to make the models smarter over time.

Consequently, we can anticipate that the in-car cameras will be able to detect facial emotions more accurately, thereby preventing accidents by a substantial margin. Computer Vision will alter how the world views autonomous vehicles, from seatbelt monitoring to developing dependent pedestrian tracking modules in 2022.

2. Content organization

Computer vision systems currently assist with content organization. Apple Photos is a prime illustration. The application has access to our photo collections, automatically adds tags to photos, and enables us to navigate a more organized collection of images. Apple Photos is a terrific tool since it automatically offers a curated display of your favorite memories.

3. Facial recognition

Face-to-face photographs of people’s faces match their identities using facial recognition technology, p. This technology is incorporated into significant, daily-use items. For instance, Facebook uses computer vision to identify individuals in photographs.

Face recognition is significant biometric authentication technology. Numerous mobile gadgets on the market today permit users to unlock their devices by presenting their faces. For facial recognition, a front-facing camera is utilized; mobile devices scan this image and, depending on analysis, determine whether the person holding a device is authorized to use it. The speed at which this technology operates is its greatest asset.

4. Touch commerce

It may have looked like science fiction a few years ago, but it is now possible to purchase anything with the tap of a finger. Touch commerce combines touchscreen technology and one-click buying to allow users to purchase things straight from their mobile devices. Clients can buy anything from clothing to furnishings after linking payment information to a general account and activating the service.

This is one of the most significant eCommerce developments in recent years, with sales of this type predicted to increase by 150 percent this year alone and retailers in practically every industry anticipating a gain in revenue from this new technology.

5. Augmented reality

Computer vision is crucial to augmented reality applications. This technology enables augmented reality (AR) applications to detect physical items (both surfaces and individual objects inside a given physical location) in real-time and utilize this data to position virtual objects within the physical surroundings.

6. Self-driving Automobiles

Computer vision enables automobiles to comprehend their environment. Several cameras on an intelligent vehicle capture videos from various angles and provide them as an input signal to the computer vision software. The technology scans the video in real-time and detects road markings, nearby objects (such as pedestrians or other vehicles), traffic lights, etc. One of the most noteworthy implementations of this technology is the autopilot feature in Tesla vehicles.

7. Healthcare

In Healthcare, computer vision has been making waves. In 2022, however, we anticipate that this AI application will collaborate with the likes of Deep Learning to assist medical startups in developing highly proactive tools and machines, with a focus on identifying critical diseases more rapidly, measuring blood loss accurately, enhancing diagnostic accuracy, and even providing better medical imaging standards.

8. Agriculture

Numerous agricultural organizations utilize computer vision to analyze the harvest and resolve typical agricultural issues, such as weed emergence and nutrient insufficiency, with the help of computer vision. Computer vision systems analyze images captured by satellites, drones, or aircraft to spot problems early, preventing excessive financial losses.

9. Edge Computing

In 2022, Edge Computing will surpass Cloud Computing in specific applications, mainly when data privacy is paramount. In addition, with edge computing dependent on on-premises tools and real-time connections between both the source and origin, computer vision in 2022 will strive to provide faster replies.

In the following months, the widespread deployment of Edge Computing will make Computer Vision a standard technology, decreasing the current latency between data identification, data categorization, and data interpretation.

Final Remarks

In addition to the industries mentioned above, Computer Vision will impact surveillance, data annotation, three-dimensional imaging, manufacturing, and supply chain management in 2022. With machines becoming increasingly clever with each passing day, AI and Machine Learning will make life easier for companies and customers in the current and succeeding years, both in the immediate and far future.

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