Research Advances in Deep Learning for Image Semantic Segmentation Techniques

Image semantic segmentation represents a significant area of research within the field of computer vision. With the advent of deep learning, image semantic segmentation techniques that integrate deep learning have demonstrated superior accuracy compared to traditional image semantic segmentation methods. Recently, the Mamba architecture has demonstrated superior semantic segmentation performance compared to the Transformer architecture, and has consequently become a research focus in this field. Nevertheless, the specifics of the Mamba architecture have remained underexplored in the extant literature. This review provides a comprehensive overview of the latest research progress in deep learning techniques for semantic segmentation. It offers a systematic review of traditional convolutional neural network (CNN)-based architectures and focuses on a series of emerging architectures, including the Transformer architecture, the Mamba architecture, and cutting-edge approaches such as self-supervised learning strategies. For each category, a detailed account is provided of the principal algorithms and techniques employed, together with a report on the performance achieved using datasets commonly used in the field.