This article discusses the six use cases of image annotation for self-driving cars. Read on to find out more about this latest news in AI.<\/b>
\nJoin the DZone community and get the full member experience. In autonomous driving, computer vision is an important role in making the varied objects recognizable. And there are different types of image annotation techniques used to annotate and make the objects recognizable to machine learning and deep learning. And not only objects but making the whole scenario including road lanes, street lights, other vehicles, and other objects visible in their natural environment. And for each type of object, there are different types of image annotation techniques are used. So, here today we will discuss the six use cases of image annotation for self-driving car or autonomous vehicle driving. 2D image annotation or bounding box annotation is used to make the objects like other vehicles recognizable with a second dimension. It is one of a simple but most popular image annotation technique helps to detect and recognize the objects for an autonomous vehicle. It is one of the most crucial image annotation technique helps to detect the accurate position of the object. Yes,3D point cloud annotation is done for LiDAR sensing-based self-driving cars that can make the object recognizable from a distant place with the highest level of accuracy. ADAS of Automated Driving Assistance System also works with semi-autonomous driving features. Such cars can sense their surroundings and keep an eye on drivers and their actions like eyes movement or feeling drowsiness. Image annotation is also performed to make such actions recognizable for semi-automatic cars. Again bounding box annotation is used to annotate to train the ADAS. As there are different types of objects or similar objects that need to be classified to make them different from each other. Semantic segmentation image annotation is the more precise annotation technique that helps classify the object of the single class, it can make the similar class objects recognizable to autonomous vehicles with the highest level of accuracy. Apart from various objects, for self-driving cars, lane detection is also important to move in the right direction. Polyline Annotation is used to make the lane on the road recognizable. Polyline, Spline, and Simple Line annotation are drawn on the road helping the autonomous vehicles drive on the right path. And for different types of the road like single lane road or double lane road different types of annotation technique is used to create the self-driving car training data. Cogito provides the image annotation service to create the training data for autonomous vehicle driving. Working with a team of well-trained and highly skilled annotators to annotate the images and videos with the next level of precision producing high-quality training data for deep learning. It is known for offering world-class training data sets for machine learning and deep learning at the lowest cost. Published at DZone with permission of Roger Brown. See the original article here. Opinions expressed by DZone contributors are their own.<\/p>\n