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 of the objects. Self-driving cars need to visualize the depth of the objects with accurate dimensions.
Semantic Segmentation to Classify Objects for Self-driving Cars
This is another useful and very helpful technique to detect the objects in the single class. In semantic image annotation technique, objects are annotated with shaded to make it recognizable to visual perception based AI-model like self-driving cars or autonomous vehicles.
3D Point Cloud Annotation for LiDARs Detections
This annotation technique is one the best and comprehensive image annotation techniques used to train the AI-based LiDAR sensors in self-driving cars. To distinguish and classify different types of lanes on road in 3D point cloud map, this image annotation method is used for self-driving car training.
Polygon Annotation to Detect Irregular Shaped Objects
While training the self-driving cars algorithms, coarse or irregular shaped objects like surface roadmarking on the street need to make recognizable to such automated machines. Polygon annotation helps to recognize various other objects visible on the street like cars or motor vehicles.
Polyline Annotation for Lane Detection in Self-driving Cars
Polyline image annotation is basically, the technique of creating the training dataset for self-driving cars to detect the lane on the road. In this annotation, the road lanes like single lane, broken lane and double lane are annotated to make it recognizable to visual perception AI model for autonomous vehicle.
Get Self Driving Car Training Data with Anolytics
Anolytics provides self driving car training data with the best quality. It is annotating the huge amount of images containing the objects on the street with next level of accuracy. It is providing the best image annotation service to train machine learning or deep learning AI model like autonomous vehicles helping to train with precision for right moving on the road.