Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022)

<Previous Article In Volume
Next Article In Volume>

Assessing Deep Learning Model Using AlexNet for Water Traffic Counting in Martapura River

Authors
Nahdi Saubari1, *, Wang Kunfeng1
1College of Information Science and Technology, Program of Controlling Science and Engineering, Beijing University of Chemical Technology, Beijing, China
*Corresponding author. Email: nahdi.vfp@gmail.com
Corresponding Author
Nahdi Saubari
Available Online 9 November 2023.
DOI
10.2991/978-94-6463-284-2_41How to use a DOI?
Keywords
Traffic Counting; Martapura River; AlexNet
Abstract

In recent years, the traffic of water transportation in Martapura river has been increased and creating many problems for the city and its environment. Hence, the traffic needs to be managed from time to time. Deep learning model might be used for traffic counting by detecting the ships. This study aims to assess AlexNet for traffic counting purposes in Martapura river. Data were collected two times a day for 3 months by using smartphone camera. Series of experiments were developed using Alexnet model to classify and detect ships or boats in Martapura River to draw a baseline for water traffic counting system. Result shows that Alexnet gives around 97% accurateness in detecting ships or other water vehicle as the main transportation. This certainly helps the traffic counting in Martapura river. Around 5 to 7 water vehicles were detected per hour. AlexNet also detect other floating objects like water plantation or plastic garbage. Other than object detection, AlexNet as Deep Learning technology can be used for water traffic counting globally.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume
Next Article In Volume>
Volume Title
Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022)
Series
Advances in Engineering Research
Publication Date
9 November 2023
ISBN
10.2991/978-94-6463-284-2_41
ISSN
2352-5401
DOI
10.2991/978-94-6463-284-2_41How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Nahdi Saubari
AU  - Wang Kunfeng
PY  - 2023
DA  - 2023/11/09
TI  - Assessing Deep Learning Model Using AlexNet for Water Traffic Counting in Martapura River
BT  - Proceedings of the 4th Borobudur International Symposium on Science and Technology 2022 (BIS-STE 2022)
PB  - Atlantis Press
SP  - 355
EP  - 364
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-284-2_41
DO  - 10.2991/978-94-6463-284-2_41
ID  - Saubari2023
ER  -