Getting Started With Semantic Segmentation Using Deep Learning

Segmentation is essential for image analysis tasks. Semantic segmentation describes the process of associating each pixel of an image with a class label, (such as flower, person, road, sky, ocean, or car).

Applications for semantic segmentation include:

• Autonomous driving

• Industrial inspection

• Classification of terrain visible in satellite imagery

• Medical imaging analysis

Train a Semantic Segmentation Network

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

1. Analyze Training Data for Semantic Segmentation

2. Create a Semantic Segmentation Network

3. Train A Semantic Segmentation Network

4. Evaluate and Inspect the Results of Semantic Segmentation

Label Training Data for Semantic Segmentation

Large datasets enable faster and more accurate mapping to a particular input (or input aspect). Using data augmentation provides a means of leveraging limited datasets for training. Minor changes, such as translation, cropping, or transforming an image provides new distinct and unique images. See Augment Images for Deep Learning Workflows Using Image Processing Toolbox (Deep Learning Toolbox)

You can use the Image Labeler app to interactively label pixels and export the label data for training. The app can also be used to label rectangular regions of interest (ROIs) and scene labels for image classification.