Segmentation is essential for image analysis tasks. Semantic segmentation is image classification at a pixel level. Semantic segmentation describes the process of associating each pixel of an image with a class label. In an image that has many cars, segmentation will label all the objects as car objects. Semantic segmentation is very crucial in self-driving cars and robotics because it is important for the models to understand the context in the environment in which they are operating.

The steps for training a semantic segmentation network are as follows:

  • Analyze training data for semantic segmentation
  • Create a semantic segmentation network
  • Train a semantic segmentation network
  • Evaluate and inspect the results of semantic segmentation

Applications for semantic segmentation include:

  • Autonomous driving
  • Industrial inspection
  • Classification of terrain visible in satellite imagery
  • Medical imaging analysis
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