Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN

Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN

This study proposed a multimodal vehicle classification approach using a hybrid shallow CNN architecture that integrates radar and acoustic sensor data to overcome the limitations of single-sensor systems. The shallow CNN includes convolutional layers with max-pooling and fully connected layers, emp...

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Published in:IEEE Symposium on Wireless Technology and Applications, ISWTA
Main Author: Yazli A.Y.; Mohd Shariff K.K.; Rahman S.A.M.A.J.S.A.; Md Ali M.A.
Format: Conference paper
Language:English
Published: IEEE Computer Society 2024
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203821523&doi=10.1109%2fISWTA62130.2024.10652023&partnerID=40&md5=ee05f2cc9356147085863b5b1b356055
id 2-s2.0-85203821523
spelling 2-s2.0-85203821523
Yazli A.Y.; Mohd Shariff K.K.; Rahman S.A.M.A.J.S.A.; Md Ali M.A.
Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
2024
IEEE Symposium on Wireless Technology and Applications, ISWTA


10.1109/ISWTA62130.2024.10652023
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203821523&doi=10.1109%2fISWTA62130.2024.10652023&partnerID=40&md5=ee05f2cc9356147085863b5b1b356055
This study proposed a multimodal vehicle classification approach using a hybrid shallow CNN architecture that integrates radar and acoustic sensor data to overcome the limitations of single-sensor systems. The shallow CNN includes convolutional layers with max-pooling and fully connected layers, employing concatenation or summation operators for data fusion at early or late stages. Evaluated on a dataset of 3300 paired samples across five vehicle classes, with spectrogram images as input, the proposed method yielded promising results. Late fusion with concatenation outperformed early fusion and summation. The highest F1-scores were 99%-100% for lorry, motorcycles, buses, and no traffic, and 97% for cars. This multimodal approach demonstrated its potential for intelligent transportation systems and traffic monitoring applications. © 2024 IEEE.
IEEE Computer Society
23247843
English
Conference paper
author Yazli A.Y.; Mohd Shariff K.K.; Rahman S.A.M.A.J.S.A.; Md Ali M.A.
spellingShingle Yazli A.Y.; Mohd Shariff K.K.; Rahman S.A.M.A.J.S.A.; Md Ali M.A.
Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
author_facet Yazli A.Y.; Mohd Shariff K.K.; Rahman S.A.M.A.J.S.A.; Md Ali M.A.
author_sort Yazli A.Y.; Mohd Shariff K.K.; Rahman S.A.M.A.J.S.A.; Md Ali M.A.
title Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
title_short Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
title_full Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
title_fullStr Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
title_full_unstemmed Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
title_sort Multimodal Vehicle Classification based on Radar and Acoustic Sensors using Hybrid Shallow CNN
publishDate 2024
container_title IEEE Symposium on Wireless Technology and Applications, ISWTA
container_volume
container_issue
doi_str_mv 10.1109/ISWTA62130.2024.10652023
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203821523&doi=10.1109%2fISWTA62130.2024.10652023&partnerID=40&md5=ee05f2cc9356147085863b5b1b356055
description This study proposed a multimodal vehicle classification approach using a hybrid shallow CNN architecture that integrates radar and acoustic sensor data to overcome the limitations of single-sensor systems. The shallow CNN includes convolutional layers with max-pooling and fully connected layers, employing concatenation or summation operators for data fusion at early or late stages. Evaluated on a dataset of 3300 paired samples across five vehicle classes, with spectrogram images as input, the proposed method yielded promising results. Late fusion with concatenation outperformed early fusion and summation. The highest F1-scores were 99%-100% for lorry, motorcycles, buses, and no traffic, and 97% for cars. This multimodal approach demonstrated its potential for intelligent transportation systems and traffic monitoring applications. © 2024 IEEE.
publisher IEEE Computer Society
issn 23247843
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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