Review of Semantic Segmentation by Using Deep learning methods

Semantic segmentation, a critical task in computer vision, involves pixel-level classification of images to assign each pixel to a specific semantic category. Over the years, numerous authors have extensively explored and applied semantic segmentation techniques across diverse domains. This paper provides a brief overview of the highlights of its prominent role in advancing image understanding. Authors have proposed several pioneering architectures specifically tailored for semantic segmentation. The subsequent years saw the development of variants such as SegNet and Deep Lab, each addressing unique challenges in semantic segmentation tasks. Semantic segmentation finds applications in diverse fields, including medical imaging, autonomous vehicles, and augmented reality. Authors have demonstrated its utility in tasks such as tumor detection, road scene understanding, and object recognition, showcasing its versatility and potential societal impact. Recent trends include the integration of semantic segmentation with other computer vision tasks, such as instance segmentation and panoptic segmentation. Authors are exploring methods for improving interpretability, robustness to domain shifts, and efficiency in resource-constrained environments.