Data Annotation and Labeling Services for Machine Learning

Self-driving cars need to get trained with right amount of data sets so that it can detect various objects precisely and move safely in the right direction. Actually, such training dataset are created through images, in which objects are annotated precisely for accurate detection of such objects. And there are different types of image annotation technique to create such datasets.  Bounding Box for Self-driving Cars to Detect Objects Bounding box annotation helps to detect the objects in the single frame. It is mainly annotated in rectangle or square shape, to make each object detected and recognizable to machines. Basically, it is used to capture the other vehicles moving on the road and various standing objects.      3D Cuboids to Detect Objects Dimensions for Self-driving Cars 3D cuboid annotation is another technique, helps to detect the objects with its dimension. This image annotation technique helps computer vision to detect the true dimension ...

Autonomous driving systems are used to develop the self-driving cars that can operate itself without human instructions. It seems an amazing to see if a vehicle moves automatically while following the traffic rules and regulations for safe driving. But developing such autonomous driving systems is not possible until and unless the model learns everything is visible on the roads. Actually, there are multiple types of objects also addressable on streets and automated cars must detect that from certain distance to take next action like slowing down the speed, barking the vehicle or taking a turn. So, question posted right here from where autonomous driving systems get their training data to develop a full-functional model for such projects.