Deep-Learning-Based Approaches for Semantic Segmentation of Natural Scene Images: A Review

Busra Emek Soylu, Mehmet Serdar Guzel, Gazi Erkan Bostanci, Fatih Ekinci, Tunc Asuroglu (Corresponding Author), Koray Acici

Research output: Contribution to journalReview Articlepeer-review

19 Citations (Scopus)

Abstract

The task of semantic segmentation holds a fundamental position in the field of computer vision. Assigning a semantic label to each pixel in an image is a challenging task. In recent times, significant advancements have been achieved in the field of semantic segmentation through the application of Convolutional Neural Networks (CNN) techniques based on deep learning. This paper presents a comprehensive and structured analysis of approximately 150 methods of semantic segmentation based on CNN within the last decade. Moreover, it examines 15 well-known datasets in the semantic segmentation field. These datasets consist of 2D and 3D image and video frames, including general, indoor, outdoor, and street scenes. Furthermore, this paper mentions several recent techniques, such as SAM, UDA, and common post-processing algorithms, such as CRF and MRF. Additionally, this paper analyzes the performance evaluation of reviewed state-of-the-art methods, pioneering methods, common backbone networks, and popular datasets. These have been compared according to the results of Mean Intersection over Union (MIoU), the most popular evaluation metric of semantic segmentation. Finally, it discusses the main challenges and possible solutions and underlines some future research directions in the semantic segmentation task. We hope that our survey article will be useful to provide a foreknowledge to the readers who will work in this field.
Original languageEnglish
Article number2730
JournalElectronics
Volume12
Issue number12
DOIs
Publication statusPublished - 19 Jun 2023
MoE publication typeA2 Review article in a scientific journal

Keywords

  • CNN
  • computer vision
  • CRF
  • deep learning
  • general
  • indoor
  • MRF
  • outdoor
  • SAM
  • semantic segmentation
  • street scenes
  • UDA

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