The age of autonomous vehicles is coming! And image recognition technology has a critical role in it!
After all, image recognition can make self-driving or semi-self-driving cars safe, reliable, and more efficient!
Remember Will Smith’s “I, Robot” or Bruce Willis’ “The Fifth Element? We bet you do!
After all, these Sci-Fi movies from decades back have already made us witness what the world with autonomous cars would look like!
But in reality, we have just started to catch up with the concept of self-driving cars! Though many tech giants like Tesla, Ford, Apple, and many more companies like Google have created prototypes already!
And no conversation about the automotive industry, especially self-driving cars, can be complete without it!
So, is your company or organization involved in designing, developing, manufacturing, selling, marketing, or maintaining motor vehicles, and would like to continue doing so in the coming years? Then time to know how image recognition is transforming the automotive industry!
Image recognition refers to a technology or the ability of software to identify people, objects, places, actions, and writings in images.
A subset of Computer Vision, image recognition aims to help automate the performance of a task through processing, retrieving, interpreting, and examining pictures and presenting useful information in different formats.
Does this tech sound familiar to you? Why not! After all, you use it!
Remember when you upload an image on Facebook, and Facebook automatically suggests tags for the people in the photograph? Well, that is an API at play!
Even when it comes to taking the digital fingerprint of a person to determine the person’s identity, then again, it’s AI image recognition tech in use.
And now, with each passing day, this technology is growing rapidly and becoming more adept at mimicking human understanding and vision!
We are yet to see autonomous vehicles taking the road entirely! But we cannot disregard the prototypes of self-driving cars that leading tech companies have created already!
Some of these cars use a laser-based technology called Light Detection and Ranging or LiDaR technology to 3D-map the surrounding environment. Similar to sonars, this laser-based 3D-mapping technology can detect objects, street furniture, slopes, and more.
But unfortunately, this tech lacks the ability to predict. Also, this technology is lagging due to the delay in the light coming back to the receiver and evaluating the newly created data points.
And that is where, as suggested by the CEO of Tesla, Elon Musk, more focus is on improving the cameras and AI-based image recognition technology.
Especially when companies like Apple have already picked up on this idea, let’s see how image recognition in cars can benefit and transform the automotive industry!
Self-powered or self-driving vehicles are an emerging market. Over the last few years, the automotive industry has been incorporating various smart facilities and advanced technologies, and image recognition is one of them.
To know how exactly embracing image recognition can help the industry, go ahead, and read on!
Do you know computer vision, often used interchangeably with image recognition, can be instrumental in preventing crashes?
Yes, computer vision can help implement some vital safety measures in cars.
For example, with the help of computer vision (image recognition software), the LDW or the Lane Departure Warning systems can send warnings to the driver if or when the vehicle moves out of its designated lane. The LDW systems can also identify when another car enters the current lane without giving the turn signal and trigger a warning to the driver to avoid a collision. It can even activate automatic braking systems and be super helpful when drivers get distracted.
Plus, vision systems automotive (mounted on a car) can help drivers avoid imminent dangers while driving during the night by enhancing the night vision mode and making the driver see as clearly as during the day.
A CV system can also monitor the biometrics and reactions of the driver and analyze whether there’s a chance for the driver to fall asleep. For example, if it detects the driver is under the influence of a substance, it will block the car to prevent accidents.
Even when it comes to preventing accidents involving pedestrians, the CV system of a vehicle can monitor the behavior of a pedestrian, identify it and alert the driver if there is a chance the pedestrian can do anything unsafe and not permitted, like crossing the road while the signal is still red.
Preventing accidents in self-powered cars is the top priority of automotive companies. Even in conventional vehicles, making the assistance systems smart enough to avoid accidents matters the most.
But we can’t ignore the efficiency factor, can we?
So whether it’s self-driving cars or semi-autonomous cars, along with taking care of driver’s comfort and safety, preventing getting stuck in traffic jams is also important.
And that is where AI image recognition can help!
Since fully autonomous or semi-autonomous vehicles can communicate with each other, image recognition can help with better resource allocation and route optimization and thus reduce the chances of urban traffic. Autonomous cars can scan for possible dangers using AI-powered image recognition and transmit the same information to other vehicles to reduce the risks of traffic jams caused due to accidents.
More than 46,000 people die in car crashes every year in the U.S. So, if technologies like image recognition can contribute, in some way, to reducing this number while boosting a car’s efficiency, why not!
Also, if we consider fuel efficiency (for electric motors), image recognition technology contributes to that, too! Since image recognition in cars can help to map the terrain ahead, based on that information, a computer can determine the ideal power consumption, especially when it comes to driving in the hills or valleys.
To make sure that the self-powered cars are safe, they need to quickly identify the hazards and make informed decisions about what steps to take next to prevent casualties. And that is where image recognition comes in!
Using image recognition, the sensors of self-powered cars can spot dangers on the road the same way a human would while driving. And that’s not it. The sensor will respond to the dangers the same way a human would react to prevent an accident or crash.
Plus, the latest technologies, like image recognition machine learning, are advancing the production process in the automobile industry and ensuring increased success rates for businesses while improving people’s lives.
For example, the leading luxury vehicle company BMW has been leveraging AI since 2018. It has been focusing on AI-powered automated image recognition technology to evaluate component images in onboard production and then compare those images in milliseconds to maintain the high-quality standards of its products and save its people from performing repetitive work.
The company also uses image recognition technology to identify moving objects or interfering factors like the lighting in the production area.
Yes, we are still away from witnessing self-driving cars dominating the roads! But image recognition is already here, playing a crucial part in the automotive industry and working towards turning the days of the future a reality! After all, most cars are now capable of doing more than just self-driving and self-parking!
So if you are into the automotive industry, take action now and implement AI-powered solutions like image recognition technology to make your business processes and cars more reliable, efficient, and safer than ever!
For top-notch image recognition machine learning, computer vision, and AI solutions empowered with image recognition features, Klizo Solutions is just a call away. Connect with us today to find out more or get a free quote for your project!
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Klizo Solutions was founded by Joseph Ricard, a serial entrepreneur from America who has spent over ten years working in India, developing innovative tech solutions, building good teams, and admirable processes. And today, he has a team of over 50 super-talented people with him and various high-level technologies developed in multiple frameworks to his credit.