Autonomous driving

Powering self-driving models with the highest quality training data and accelerating ML pipelines.

computer vision in autonomous driving

Data annotation for self-driving vehicles

computer vision in autonomous driving

3D cuboids

Outline the objects in images and videos with labeling on depth, width, and length.
computer vision in autonomous driving

Polygons

Perform pixel-cut annotations for accurate object detection and localization in images and videos.
semantic segmentation

Semantic segmentation

Divide the image into clusters to seamlessly classify objects like cars, bikes, pedestrians, sidewalks, traffic lights, etc.
computer vision in autonomous driving

Polyline

Annotate line segments such as wires, lanes,and sidewalks to train a lane detection model.
computer vision in autonomous driving

Video object tracking

Annotate videos to helpyour model detect objects and follow their movement.
computer vision in autonomous driving

Street sign detection

Train your model to recognize and read street signs to bypass accidents on the road.

Computer vision challenges in autonomous vehicles: The future of AI

It’s no doubt that the autopilot system is one of the most significant accomplishments in machine learning that projects the future reality. It is expected that self-driving cars will become common place in the span of 10 years. Yet, with the growing demand for autonomous vehicles, there are also emerging challenges for computer vision.

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computer vision in autonomous driving

SuperAnnotate for autonomous driving industry

The companies developing autonomous driving technologies heavily rely on machine learning to achieve greater goals and outdo the competitors. However, this kind of complex, sophisticated models require a robust data annotation workflow with quality management measures and smooth iteration cycles. SuperAnnotate is designed to feed quality data into AI models and get them into production up to 5x faster.

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