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A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications

Masood, Sharjeel; Ahmed, Fawad; Alsuhibany, Suliman A.; Ghadi, Yazeed Yasin; Siyal, M. Y.; Kumar, Harish; Khan, Khyber; Ahmad, Jawad


Sharjeel Masood

Fawad Ahmed

Suliman A. Alsuhibany

Yazeed Yasin Ghadi

M. Y. Siyal

Harish Kumar

Khyber Khan


In recent years, the development of smart transportation has accelerated research on semantic segmentation as it is one of the most important problems in this area. A large receptive field has always been the center of focus when designing convolutional neural networks for semantic segmentation. A majority of recent techniques have used maxpooling to increase the receptive field of a network at an expense of decreasing its spatial resolution. Although this idea has shown improved results in object detection applications, however, when it comes to semantic segmentation, a high spatial resolution also needs to be considered. To address this issue, a new deep learning model, the M-Net is proposed in this paper which satisfies both high spatial resolution and a large enough receptive field while keeping the size of the model to a minimum. The proposed network is based on an encoder-decoder architecture. The encoder uses atrous convolution to encode the features at full resolution, and instead of using heavy transposed convolution, the decoder consists of a multipath feature extraction module that can extract multiscale context information from the encoded features. The experimental results reported in the paper demonstrate the viability of the proposed scheme.


Masood, S., Ahmed, F., Alsuhibany, S. A., Ghadi, Y. Y., Siyal, M. Y., Kumar, H., …Ahmad, J. (2022). A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications. Wireless Communications and Mobile Computing, 2022, Article 8684138.

Journal Article Type Article
Acceptance Date Jun 2, 2022
Online Publication Date Jun 18, 2022
Publication Date Jun 18, 2022
Deposit Date Jun 27, 2022
Publicly Available Date Jun 27, 2022
Journal Wireless Communications and Mobile Computing
Print ISSN 1530-8669
Electronic ISSN 1530-8669
Publisher Wiley
Peer Reviewed Peer Reviewed
Volume 2022
Article Number 8684138
Public URL


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