Deep learning has provided great accuracy in the field of computer vision, particularly for object detection. In the past, segmenting images was done by partitioning images into grab cuts, superpixels, and graph cuts. The main problem with the traditional process was that the algorithm was unable to recognize parts of the images.
On the other hand, semantic segmentation algorithms aim to divide the image into relevant categories. They associate every pixel in an input image with a class label: person, tree, street, road, car, bus, and so on. Semantic segmentation algorithms are dynamic and have many use cases, including self-driving cars (SDCs).
In this chapter, you will learn how to perform semantic segmentation using OpenCV, deep learning, and the ENet architecture. As you read this chapter...
