A Review on Deep Learning Techniques Applied to Semantic Segmentation

TL;DR

This paper reviews deep learning techniques for semantic segmentation, covering terminology, datasets, challenges, methods, results, and future directions in the field.

Contribution

It provides a comprehensive overview of current deep learning approaches for semantic segmentation, highlighting their significance and recent advancements.

Findings

Deep learning methods have significantly improved segmentation accuracy.

Various datasets and challenges influence method development.

Future research should focus on real-time and scalable solutions.

Abstract

Image semantic segmentation is more and more being of interest for computer vision and machine learning researchers. Many applications on the rise need accurate and efficient segmentation mechanisms: autonomous driving, indoor navigation, and even virtual or augmented reality systems to name a few. This demand coincides with the rise of deep learning approaches in almost every field or application target related to computer vision, including semantic segmentation or scene understanding. This paper provides a review on deep learning methods for semantic segmentation applied to various application areas. Firstly, we describe the terminology of this field as well as mandatory background concepts. Next, the main datasets and challenges are exposed to help researchers decide which are the ones that best suit their needs and their targets. Then, existing methods are reviewed, highlighting their contributions and their significance in the field. Finally, quantitative results are given for the described methods and the datasets in which they were evaluated, following up with a discussion of the results. At last, we point out a set of promising future works and draw our own conclusions about the state of the art of semantic segmentation using deep learning techniques.