A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes

4Citations
Citations of this article
15Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Semantic segmentation is the most important stage of making sense of the visual traffic scene for autonomous driving. In recent years, convolutional neural networks (CNN)-based methods for semantic segmentation of urban traffic scenes are among the trending studies. However, the methods developed in the studies carried out so far are insufficient in terms of accuracy performance criteria. In this study, a new CNN-based semantic segmentation method with higher accuracy performance is proposed. A new module, the Attentional Atrous Feature Pooling (AAFP) Module, has been developed for the proposed method. This module is located between the encoder and decoder in the general network structure and aims to obtain multi-scale information and add attentional features to large and small objects. As a result of experimental tests with the CamVid data set, an accuracy value of approximately 2% higher was achieved with a mIoU value of 70.59% compared to other state-of-art methods. Therefore, the proposed method can semantically segment objects in the urban traffic scene better than other methods.

References Powered by Scopus

Deep residual learning for image recognition

174363Citations
50303Readers
Get full text

U-net: Convolutional networks for biomedical image segmentation

65055Citations
15529Readers

This article is free to access.

14767Citations
7850Readers
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Doğan, G., & Ergen, B. (2024). A new CNN-based semantic object segmentation for autonomous vehicles in urban traffic scenes. International Journal of Multimedia Information Retrieval, 13(1). https://doi.org/10.1007/s13735-023-00313-5

Readers over time

‘24‘250481216

Readers' Seniority

Tooltip

Researcher 1

100%

Readers' Discipline

Tooltip

Computer Science 1

100%

Save time finding and organizing research with Mendeley

Sign up for free
0