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A Deep Learning-Based Semantic Segmentation Architecture for Autonomous Driving Applications.
- Source :
- Wireless Communications & Mobile Computing; 6/18/2022, p1-12, 12p
- Publication Year :
- 2022
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15308669
- Database :
- Complementary Index
- Journal :
- Wireless Communications & Mobile Computing
- Publication Type :
- Academic Journal
- Accession number :
- 157520938
- Full Text :
- https://doi.org/10.1155/2022/8684138