Advancements in Semantic Segmentation Using Deep Learning Approaches

Semantic segmentation plays an important part in computer vision by assigning semantic labels to each pixel in an image. Many models have been created to enhance segmentation performance as deep learning develops, from traditional models CNNs, FCNs, and U-Net, to the advanced models DeepLab series, GANs, and Transformer-based models. This paper offers a comprehensive overview of semantic segmentation methods based on deep learning. It begins with an introduction to the task and related background concepts, followed by a review of traditional and advanced models. A summary of current issues, including labeled data scarcity, complex object boundary definition, poor model generalization, and multi-scale information handling, is also provided to assist in future study and real-world application. The study also provides a comparative evaluation of the models' computational and performance characteristics. The overall goal of this work is to give a clear overview of the progress made in semantic segmentation using deep learning.