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Semantic Segmentation labeling for Autonomous Vehicles1 min read

The Semantic Segmentation or Pixel-level labeling is used to label each and every pixel in the image. Unlike polygonal segmentation devised specifically to detect a defined object of interest, full semantic segmentation provides a complete understanding of every pixel of the scene in the image. This kind of for detection and localization of specific objects in pixel-level.

Semantic Segmentation - Input/Output with Instance-aware Segmentation

There are three main use cases in semantic segmentation:

Full-pixel Semantic Segmentation for Autonomous Vehicles

  • Instance segmentation: Used for training perception models in non-environmental objects of interest
  • Full pixel semantic segmentation: Used in autonomous vehicles and safety surveillance cameras where information of every pixel is critical and may influence the accuracy of the perception model.
  • Panoptic segmentation: Used to individually segment objects of the same class by assigning instance unique IDs to each object.

Complex cases handled by Playment:

  • Dense Vegetation (with scattered patches of the sky) and differentiating between terrain and vegetation
  • Occlusion related cases – Vegetation occluding building/ vehicle/ pedestrian
  • Wires across the sky
  • Lanes and signs

See how Playment differs from traditional crowdsourcing.

As semantic segmentation involves pixel-wise accuracy, it has the highest time and cost per annotation. Playment is a fully managed data labeling platform generates high-quality annotations at scale. We combine both machine learning and human intelligence in a single platform for enabling better autonomous vehicle systems.

Part 3: Improve Facial Recognition using Semantic Segmentation

Know more about the Image annotation services by Playment here.