Semantic Segmentation for Various Applications: Research Contribution and Comprehensive Review †

Abstract

Semantic image segmentation is used to analyse visual content and carry out real-time decision-making. This narrative literature analysis evaluates the multiple innovations and advancements in the semantic algorithm-based architecture by presenting an overview of the algorithms used in medical image analysis, lane detection, and face recognition. Numerous groundbreaking works are examined from a variety of angles (e.g., network structures, algorithms, and the problems addressed). A review of the recent development in semantic segmentation networks, such as U-Net, ResNet, SegNet, LCSegnet, FLSNet, and GNet, is presented with evaluation metrics across a range of applications to facilitate new research in this field.