Vehicles that are autonomous or semi-autonomous are equipped with a variety of technology that significantly improves the driving experience. The existence of several cameras, sensors, and other systems makes this possible. A tonne of data is produced by all of these elements. The Advanced Driver Assistance Systems(ADAS), which relies on computer vision, is one such instance. It makes use of a computer to understand the visuals at a high level and warn the driver by helping him make better decisions by assessing various situations.
Why is annotation used?
The numerous sensors and cameras found in modern vehicles generate a lot of data. These data sets cannot be used effectively unless they are correctly labeled so that they can be processed further. In order to create training models for autonomous vehicles, these data sets must be employed as a component of a testing suite. The data can be labeled using various automation methods because doing so by hand would be incredibly laborious.
Data annotation and AV safety
We are contrasting viewpoints when we contrast a computer-driven car with a human-driven car. The National Highway Traffic Safety Administration in the US estimates that there are more than six million auto accidents each year. In these collisions, more than 36,000 Americans perish, and another 2.5 million end up in hospital emergency rooms. Even more astounding are the figures on a worldwide scale. Annotation can be done using polygons, boxes, and polylines. Different modes namely interpolation, attribute annotation mode, and segmentation among others.
Types of data annotation
Data annotation is the process of tagging or classifying objects captured in a frame by an AV. Deep learning models are fed with this material that has been further curated, manually labeled or tagged, or both. In order for AVs to learn to see patterns in data and effectively classify in order to make the best conclusion, this approach is necessary. In
order to get the best possible data, it is crucial to use the proper type of annotation. Some of the various data annotation kinds for AVs are as follows:
- Bounding Box Annotation: Marking rectangular boxes to identify targeted objects
- Semantic Segmentation: Annotating images after segmenting into component parts.
- Polygon Annotation: Annotating an object’s exact edges, regardless of shape
- Object Tracking: Locating and tracking objects across a series of images or point clouds in sequential frames
The future
Driverless cars are already on some highways, altering transportation as a result of the tremendous improvements brought on by the push for AVs. Innovative thinkers will always need access to high-quality, affordable data to advance at this rate. We have a huge chance to work with people, processes, and technology to deliver the greatest datasets as data annotation experts. Data annotation suppliers and developers must innovate to address edge circumstances and create data-driven systems that are impenetrable and perceptive if AVs are to become a mainstream reality.
About Data Labeler
By leveraging the advanced tools and technologies, Data Labeler offers best-in-class data labeling services in computer Vision projects. We at Data labeler believe in providing jobs to underserved communities and making them financially independent. We are on a mission to help them earn a living through the major changes brought by AI & ML, empowering businesses all over the world.
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