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