Semantic Segmentation Models for Autonomous Vehicles

In a previous post, we studied various open datasets that could be used to train a model for pixel-wise semantic segmentation of urban scenes. Here, we take a look at various deep learning architectures that cater specifically to time-sensitive domains like autonomous vehicles. In recent years, deep learning has surpassed traditional computer vision algorithms by learning a hierarchy of features from the training dataset itself. This eliminates the need for hand-crafted features and thus such techniques are being extensively explored in academia and industry. Prior to deep learning architectures, semantic segmentation models relied on hand-crafted features fed into classifiers like Random Forests, SVM, etc.