A review on current progress of semantic segmentation

Abstract

Semantic segmentation, as an important task in the field of computer vision, has wide applications in image analysis and scene analysis. These application domains include autonomous driving, medical image analysis, image identification, and intelligent video surveillance. However, it faces many challenges due to the complex image structures and some confusing relationships between objects. This paper aims to provide an overview of key concepts in the field of semantic segmentation, including datasets and annotations, data augmentation, some relevant algorithms and models, and loss functions. By introducing and analyzing these concepts, we can gain a comprehensive understanding of the research progress and future directions in semantic segmentation. This paper also provides research advancements in the field of semantic segmentation. Through the introduction and analysis of these different concepts, we gain a deeper understanding of the current state and challenges of semantic segmentation. With the continuous development of deep learning techniques, we can expect semantic segmentation to have broader applications in the fields of computer vision and artificial intelligence.