Six Use Cases of Image Annotation in Autonomous Driving

by Matthew Mcmullen Manager

In autonomous driving the 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 lane, street lights, other vehicles and other objects visible in their natural environment. And for each types of objects there are different types of image annotation technique is used. So, here today we will discuss about the six use cases of image annotation for self-driving car or autonomous vehicle driving.

Dimension Detection with 3D Cuboid

2D image annotation or bounding box annotation is used to make the objects like other vehicles recognizable with second dimension. It is one of a simple but most popular image annotation technique helps to detect and recognize the objects for autonomous vehicle.

3D Point Cloud for LiDARs Sensing

It is one of the most crucial image annotation technique helps to detect 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 the distant place with highest level of accuracy.

Annotation for Driver Monitoring in ADAS

ADAS of Automated Driving Assistance System also work 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.

Classification Object Detection Semantic Segmentation

As there are different types of objects or similar objects that needs to be classified to make them different from each other. Semantic segmentation image annotation is the more precise annotation technique helps classify the object of the single class, it can make the similar class objects recognizable to autonomous vehicles with highest level of accuracy.

Polyline Annotation for Lane Detection

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 is drawn on the road helping the autonomous vehicles drive at right path. And for different types of 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 team of well-trained and highly skilled annotators to annotate the images and videos with next level of precision producing the high-quality training data for deep learning. It is known for offering the world-class training data sets for machine learning and deep learning at lowest cost.