PPANet: Point-Wise Pyramid Attention Network for Semantic Segmentation

In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic...

Full description

Saved in:
Bibliographic Details
Published in:Wireless communications and mobile computing 2021, Vol.2021 (1)
Main Authors: Elhassan, Mohammed A. M., Chen, YuXuan, Chen, Yunyi, Huang, Chenxi, Yang, Jane, Yao, Xingcong, Yang, Chenhui, Cheng, Yinuo
Format: Article
Language:English
Subjects:
Advanced driver assistance systems
Algorithms
Artificial neural networks
Classification
Coders
Deep learning
Design
Encoders-Decoders
Feature extraction
Modules
Neural networks
Representations
Semantic segmentation
Semantics
Training
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, convolutional neural networks (CNNs) have been at the centre of the advances and progress of advanced driver assistance systems and autonomous driving. This paper presents a point-wise pyramid attention network, namely, PPANet, which employs an encoder-decoder approach for semantic segmentation. Specifically, the encoder adopts a novel squeeze nonbottleneck module as a base module to extract feature representations, where squeeze and expansion are utilized to obtain high segmentation accuracy. An upsampling module is designed to work as a decoder; its purpose is to recover the lost pixel-wise representations from the encoding part. The middle part consists of two parts point-wise pyramid attention (PPA) module and an attention-like module connected in parallel. The PPA module is proposed to utilize contextual information effectively. Furthermore, we developed a combined loss function from dice loss and binary cross-entropy to improve accuracy and get faster training convergence in KITTI road segmentation. The paper conducted the training and testing experiments on KITTI road segmentation and Camvid datasets, and the evaluation results show that the proposed method proved its effectiveness in road semantic segmentation.
ISSN:1530-8669
1530-8677
DOI:10.1155/2021/5563875