Open Access

ARTICLE

A Survey on Image Semantic Segmentation Using Deep Learning Techniques

Jieren Cheng^1,3, Hua Li^2,*, Dengbo Li³, Shuai Hua², Victor S. Sheng⁴
1 School of Computer Science and Technology, Hainan University, Haikou, 570228, China
2 School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, 570228, China
3 Hainan Blockchain Technology Engineering Research Center, Hainan University, Haikou, 570228, China
4 Department of Computer Science Texas Tech University TX, 79409, USA
* Corresponding Author: Hua Li. Email:

Computers, Materials & Continua 2023, 74(1), 1941-1957. https://doi.org/10.32604/cmc.2023.032757

Received 28 May 2022; Accepted 12 July 2022; Issue published 22 September 2022

Abstract

Image semantic segmentation is an important branch of computer vision of a wide variety of practical applications such as medical image analysis, autonomous driving, virtual or augmented reality, etc. In recent years, due to the remarkable performance of transformer and multilayer perceptron (MLP) in computer vision, which is equivalent to convolutional neural network (CNN), there has been a substantial amount of image semantic segmentation works aimed at developing different types of deep learning architecture. This survey aims to provide a comprehensive overview of deep learning methods in the field of general image semantic segmentation. Firstly, the commonly used image segmentation datasets are listed. Next, extensive pioneering works are deeply studied from multiple perspectives (e.g., network structures, feature fusion methods, attention mechanisms), and are divided into four categories according to different network architectures: CNN-based architectures, transformer-based architectures, MLP-based architectures, and others. Furthermore, this paper presents some common evaluation metrics and compares the respective advantages and limitations of popular techniques both in terms of architectural design and their experimental value on the most widely used datasets. Finally, possible future research directions and challenges are discussed for the reference of other researchers.

---

Keywords

Deep learning; semantic segmentation; CNN; MLP; transformer

---