Motorcycle detection using deep learning convolution neural network

Motorcycle detection using deep learning convolution neural network

Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on De...

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Bibliographic Details
Published in:2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings
Main Author: Ismail F.N.; Yassin I.M.; Ahmad A.; Ali M.S.A.M.; Baharom R.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2020
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098244060&doi=10.1109%2fICSET51301.2020.9265361&partnerID=40&md5=4dc1834877874e79dfc0ac0a1024c47f
id 2-s2.0-85098244060
spelling 2-s2.0-85098244060
Ismail F.N.; Yassin I.M.; Ahmad A.; Ali M.S.A.M.; Baharom R.
Motorcycle detection using deep learning convolution neural network
2020
2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings


10.1109/ICSET51301.2020.9265361
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098244060&doi=10.1109%2fICSET51301.2020.9265361&partnerID=40&md5=4dc1834877874e79dfc0ac0a1024c47f
Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on Deep Learning Neural Network to detect motorcycles. Training involves various motorcycle models and poses with different resolutions and road conditions. The AlexNet network structure was chosen for implementation due to its proven performance in object detection tasks. Transfer learning was used to repurpose the AlexNet network for the described task. Training and classification were performed using the MATLAB Deep Learning Toolbox. Test results on our custom dataset demonstrates the effectiveness of the approach for the task. © 2020 IEEE
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper
author Ismail F.N.; Yassin I.M.; Ahmad A.; Ali M.S.A.M.; Baharom R.
spellingShingle Ismail F.N.; Yassin I.M.; Ahmad A.; Ali M.S.A.M.; Baharom R.
Motorcycle detection using deep learning convolution neural network
author_facet Ismail F.N.; Yassin I.M.; Ahmad A.; Ali M.S.A.M.; Baharom R.
author_sort Ismail F.N.; Yassin I.M.; Ahmad A.; Ali M.S.A.M.; Baharom R.
title Motorcycle detection using deep learning convolution neural network
title_short Motorcycle detection using deep learning convolution neural network
title_full Motorcycle detection using deep learning convolution neural network
title_fullStr Motorcycle detection using deep learning convolution neural network
title_full_unstemmed Motorcycle detection using deep learning convolution neural network
title_sort Motorcycle detection using deep learning convolution neural network
publishDate 2020
container_title 2020 IEEE 10th International Conference on System Engineering and Technology, ICSET 2020 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/ICSET51301.2020.9265361
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098244060&doi=10.1109%2fICSET51301.2020.9265361&partnerID=40&md5=4dc1834877874e79dfc0ac0a1024c47f
description Detecting and avoiding motorcycles on roads is important for Autonomous Vehicle (AV). This is because a majority of accidents occurring in Malaysia involve motorcycles. Detecting motorcycles is a challenging task due to its low visibility and high velocity. This research attempts to capitalize on Deep Learning Neural Network to detect motorcycles. Training involves various motorcycle models and poses with different resolutions and road conditions. The AlexNet network structure was chosen for implementation due to its proven performance in object detection tasks. Transfer learning was used to repurpose the AlexNet network for the described task. Training and classification were performed using the MATLAB Deep Learning Toolbox. Test results on our custom dataset demonstrates the effectiveness of the approach for the task. © 2020 IEEE
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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