How to Improve Computer Vision in Autonomous Vehicles using Image Annotation Services?

This is a blog post by Anolytics with the great discussion about how to improve computer vision in autonomous vehicles using image annotation services.The autonomous vehicles or self-driving cars, all view and detect various objects through computer vision technology.And image annotation services provides Such set of training data to make things recognizable to autonomous vehicles.

Actually , there are different types of image annotation techniques, and in this blog post, the leading types of image annotation methods used to create training data for autonomous are discussed.As there are different types of objects are visible in the natural environment and for each types of objects, different image annotations are used to make self-driving cars work properly.

Types of Annotations for Autonomous Vehicle Training:

2D Bounding Boxes for Object Detection

3D Cuboid Annotation for in-depth Recognition of Objects

Semantic Segmentation for Better Understanding of Surroundings

Line or Polyline Annotation for Precise Lane Detection on Road

Polygon Annotation for Irregular Shaped Objects Recognition

All the above listed are leading image annotation techniques used to create the training data sets to train the machine learning algorithms for precise detection of objects.Each image annotation type has its own applications in detection or recognizing the objects from the different perspective.As, autonomous vehicles while running on the road, see different types of objects in various scenarios.

Anolytics provide the image annotation services for autonomous vehicles or self-driving cars.It is offering all types of leading image annotation techniques for different AI models in various fields like healthcare, agriculture, retail, automotive, robotics and drones etc. Working with a team of dedicated annotators to annotate the all types of images with best level of accuracy for right predictions by the AI ​​model.