The Impact of Data Annotation on Computer Vision in the Automotive Industry
Many companies in the automotive industry, such as Tesla, Waymo, and many others, are widely considered to be pioneers in the field of application of machine vision technologies. In fact, the automotive industry is one of the largest consumers of such technology. According to analysts, the automotive industry forms 23% of the market for computer vision products in Germany. And according to VDMA, for Europe, this figure is 21%. Therefore, it is not surprising that machine vision algorithms gradually began to be used in the cars themselves, and not just at the stages of their production. Currently, they are used in autopilot and lane recognition technologies.
In today’s article, we will take a look at how cars are able to “see” the road and their surrounding environment, as well as the data annotation required to create such technology. First, let’s get an overview of what computer vision is.
What is Computer Vision?
Computer vision is a field of artificial intelligence related to image and video analysis. It includes a set of methods that give the computer the ability to “see” and extract information from what it sees. The systems consist of a photo or video camera and specialized software that identifies and classifies objects. They are able to analyze images (photos, pictures, videos, barcodes), as well as faces and emotions. Machine learning technologies are used to teach a computer to “see.” A lot of data is collected that allows you to highlight features and combinations of features for further identification of similar objects.
How is Computer Vision Used in Autonomous Vehicles?
Within the context of autonomous vehicles, computer vision cameras capture video from different angles and share it with the input. Computer vision can use this for classification and recognition. Then the system will detect objects around the car in real-time, such as traffic lights, pedestrians, and road maps. If you are wondering where you can find such vehicles, then you can check out Tesla’s advanced vehicles that offer autopilot. Such advanced technologies can improve road safety and also open up new business opportunities for companies from related industries, such as insurance, carsharing and driver training. In the near future, the automotive market, car service centers, and industries close to transport will change dramatically. This means that those who invest in such developments now will be able to take the lead in the era of the spread of innovative technologies.
In modern cars, video cameras are used not only as an alternative to the rear-view mirror but are also an important part of active safety systems. Their task, first of all, is to support anti-collision systems when objects are detected. The cameras are also used for lane keeping, for automatic recognition of traffic signs and traffic lights, and to monitor the condition of drivers. Together with radars and lidars, they are used to control unmanned vehicles. However, these are far from all possible areas of useful use of video cameras onboard a vehicle. With the development of artificial intelligence systems, with a decrease in the size and increase in the power of onboard computing facilities, an increase in the throughput of mobile communications, and the development of cloud technologies, it becomes possible to implement new services based on video cameras and computer vision.
What Computer Vision Challenges Do Researchers Need to Overcome?
One of the biggest challenges is simply assembling the needed dataset to train the machine learning algorithms. In order to understand just how big of a challenge this really is, think about all of the driving scenarios you encounter on a daily basis. How do you deal with a car suddenly stopping or a driver making an illegal turn? The autonomous vehicle needs to recognize all of these scenarios and perform the right actions. This means that countless hours of videos need to be collected and annotated so the system can learn from each situation.
Another issue is that the system needs to be able to recognize all of the objects on the road in all weather conditions. For example, if there is a snowstorm and the snow is occluding parts of the lines on the road, the system needs to logically infer where those lines are to avoid an accident. The same is true for driving at night, in fog and other low-visibility conditions. While we still have a long way to go in terms of overcoming these challenges, the good news is that with the right data annotation, the algorithms can be trained to see the road just like a human, if not better.
What is Data Annotation Necessary to Train Computer Vision Systems?
One of the main things computer vision systems are responsible for is recognizing all of the objects on the road. To better understand this, let’s take a look at the image below:
In the image, we can see cars and road markings. The cars have 3D boxes drawn around them, and the road markings are annotated with lines and splines, which allows the vehicles to understand the boundaries within which they can drive. Human data annotators would need to draw such 3D boxes and lines to train the machine learning system on thousands of images and videos.
In addition to this, data annotation projects for the automotive industry usually require 3D Point Cloud annotation. One of the ways an autonomous vehicle can recognize objects in its surrounding area is through the use of LiDAR which sends out beams of light that bounce off objects and return back to the LiDAR. This creates a 3D Point Cloud which is a digital representation of how the AI sees the physical world. Such a 3D Point Cloud would need to be annotated with both tagging and semantic segmentation to train the machine learning algorithms.
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June 17th, 2022Mindy News Blog