In this section, we'll discuss how to use a deep learning FCN to perform semantic segmentation of an image. Before diving into further details, let's clear the basic concepts.
Semantic segmentation refers to an understanding of an image at pixel level; that is, when we want to assign each pixel in the image an object class (a semantic label). It is a natural step in the progression from coarse to fine inference. It achieves fine-grained inference by making dense predictions that infer labels for every pixel so that each pixel is labeled with the class of its enclosing object or region.
DeepLab presents an architecture for controlling signal decimation and learning multi-scale contextual features. DeepLab uses an ResNet-50 model, pre-trained on the ImageNet dataset, as its main feature extractor network. However, it proposes a new residual block for multi-scale feature learning, as shown in the following diagram....
