Semantic segmentation is a fundamental task in the field of computer vision， which aims to assign a category label to each pixel in the input image. Many semantic segmentation networks have complex structures， high computational costs， and massive parameters. As a result， they introduce considerable latency when performing pixel-level scene understanding on high-resolution images. These limitations greatly restrict the applicability of these methods in resource-constrained scenarios， such as autonomous driving， medical applications， and mobile devices. Therefore， real-time semantic segmentation methods， which produce high-precision segmentation masks with fast inference speeds， receive widespread attention. This study provides a systematic and critical review of real-time semantic segmentation algorithms based on deep learning techniques to explore the development of real-time semantic segmentation in recent years. Moreover， it covers three key aspects of real-time semantic segmentation： real-time semantic segmentation networks， mainstream datasets， and common evaluation indicators. In addition， this study conducts a quantitative evaluation of the real-time semantic segmentation methods discussed and provides some insights into the future development in this field. First， semantic segmentation and real-time semantic segmentation tasks and their application scenarios and challenges are introduced. The key challenge in real-time semantic segmentation mainly lies on how to extract high-quality semantic information with high efficiency. Second， some preliminary knowledge for studying real-time semantic segmentation algorithms is introduced in detail. Specifically， this study introduces four kinds of general model compression methods： network pruning， neural architecture search， knowledge distillation， and parameter quantification. It also introduces some popular efficient CNN modules in real-time semantic segmentation networks， such as MobileNet， ShuffleNet，

EfficientNet， and efficient Transformer modules， such as external attention， SeaFormer， and MobileViT. Then， existing real-time semantic segmentation algorithms are organized and summarized. In accordance with the characteristics of the overall network structure， existing works are categorized into five categories： single-branch， two-branch， multibranch， U-shaped， and neural architecture search networks. Specifically， the encoder of a single-branch network is a single-branch hierarchical backbone network， and its decoder is usually lightweight and does not involve complex fusion of multiscale features. The two-branch network adopts a two-branch encoder structure， using one branch to capture spatial detail information and the other branch to model semantic context information. Multibranch networks are characterized by a multibranch structure in the encoder part of the network or a network with multiresolution inputs， where the input of each resolution passes through a different subnetwork. The U-shaped network has a contracting encoder and an expansive decoder， which are roughly symmetrical to the encoder. Most works of these aforementioned four categories are manually designed， while the neural architecture search networks are obtained using network architecture search technology based on the four types of architectures. These five categories of real-time semantic segmentation methods cover almost all real-time semantic segmentation algorithms based on deep learning， including CNN-based， Transformer-based， and hybrid-architecture-based segmentation networks. Moreover， commonly used datasets and evaluation indicators of accuracy， speed， and model size are introduced for real-time segmentation. We divided popular datasets into the autonomous driving scene and general scene datasets， and the evaluation indicators are divided into accuracy indicators and efficiency descriptors. In addition， this study quantitatively evaluates various real-time semantic segmentation algorithms mentioned on multiple datasets by using r

elevant evaluation indicators. To avoid the interference of different devices when conducting a quantitative comparison between real-time semantic segmentation algorithms， this study compares the performance of advanced methods of each category with the same devices and configuration and establishes a fair and integral real-time semantic segmentation evaluation system for subsequent research， thereby contributing to a unified standard for comparison. Finally， current challenges in real-time semantic segmentation are discussed， and possible future directions for improvements are envisioned （e.g.， utilization of Transformers， applications on edge devices， knowledge transfer of visual foundation models， diversity of evaluation indicators， variety of datasets， utilization of multimodal data and weakly supervised methods， combination with incremental learning）. The algorithms， datasets， and evaluation indicators mentioned in this paper are summarized at

https://github.com/xzz777/Awesome-Real-time-Semantic-Segmentation

https://github.com/xzz777/Awesome-Real-time-Semantic-Segmentation

for the convenience of subsequent researchers.