Semantic Segmentation for Self-driving Cars

One important computer vision challenge for increasing the precision and effectiveness of vehicle operations in autonomous driving scenarios is semantic segmentation for self-driving automobiles. The pixel-by-pixel assignment to distinct item categories is a crucial aspect of creating a thorough cognitive illustration of the scene. The paper offers a full overview and detailed examination of advanced segmentation of semantic image techniques based on deep learning, intended particularly for semantic segmentation in situations including autonomous driving. Usually, autonomous cars come with a list of acquisition devices so they may do a thorough scan and utilize their complementing features. A complete dataset comparison, spanning from the earliest to the most recent ones examined in this work, is provided to wrap up. In the article, recent convolutional neural network (CNN) architectures for semantic segmentation—which are fully convolutional networks—are studied first. The other two models are temporal and context-aware.