Proceedings of the 7th International Conference on Applied Engineering (ICAE 2024)

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Comparative Study of Deep Learning Algorithms Between YOLOv5 and Mobilenet-SSDv2 As Fast and Robust Outdoor Object Detection Solutions

Authors
Ryan Satria Wijaya1, *, Santonius Hasibuan1, Anugerah Wibisana1, Eko Rudiawan Jamzuri1, Mochamad Ari Bagus Nugroho2
1Department of Electrical Engineering, Politeknik Negeri Batam, Batam, Indonesia
2Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, Indonesia
*Corresponding author. Email: ryan@polibatam.ac.id
Corresponding Author
Ryan Satria Wijaya
Available Online 25 December 2024.
DOI
10.2991/978-94-6463-620-8_8How to use a DOI?
Keywords
YOLOv5; MobileNet-SSDv2; Performance Comprasion; Object Detection; Deep Learning Algorithms
Abstract

Object detection is one of the most popular applications among young people, especially millennials and Generation Z. The use of object detection has become widespread in various aspects of daily life, such as facial recognition, traffic management, and autonomous vehicles. In the implementation of object detection, large and complex datasets are required. Thus, it is important to choose an efficient object detection algorithm that yields good results. This research compares the performance of YOLOv5 and MobileNet-SSDv2 using the same dataset, demonstrating that YOLOv5 outperforms MobileNet-SSDv2 in terms of speed and accuracy in object detection. The results indicate that YOLOv5 is capable of detecting objects more rapidly and accurately compared to MobileNet- SSDv2, especially under varying lighting conditions. Several factors affecting the performance of these algorithms include the complexity of the dataset used, the available processor speed, and the memory capacity that can be utilized.

Copyright
© 2024 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.

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Volume Title
Proceedings of the 7th International Conference on Applied Engineering (ICAE 2024)
Series
Advances in Engineering Research
Publication Date
25 December 2024
ISBN
978-94-6463-620-8
ISSN
2352-5401
DOI
10.2991/978-94-6463-620-8_8How to use a DOI?
Copyright
© 2024 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  - Ryan Satria Wijaya
AU  - Santonius Hasibuan
AU  - Anugerah Wibisana
AU  - Eko Rudiawan Jamzuri
AU  - Mochamad Ari Bagus Nugroho
PY  - 2024
DA  - 2024/12/25
TI  - Comparative Study of Deep Learning Algorithms Between YOLOv5 and Mobilenet-SSDv2 As Fast and Robust Outdoor Object Detection Solutions
BT  - Proceedings of the  7th International Conference on Applied Engineering (ICAE 2024)
PB  - Atlantis Press
SP  - 94
EP  - 106
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-620-8_8
DO  - 10.2991/978-94-6463-620-8_8
ID  - Wijaya2024
ER  -