Comparative Study of YOLOv5, YOLOv7 and YOLOv8 for Robust Outdoor Detection

  • Ryan Satria Wijaya Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam,
  • Santonius Santonius Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia
  • Anugerah Wibisana Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia
  • Eko Rudiawan Jamzuri Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia
  • Mochamad Ari Bagus Nugroho Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya, Surabaya
DOI: https://doi.org/10.30871/jaee.v8i1.7207
Keywords: Deep Learning algorithms, Object detection, YOLOv5, YOLOv7, YOLOv8

Abstract

Object detection is one of the most popular applications among young people, especially among millennials and generation Z. The use of object detection has become widespread in various aspects of daily life, such as face recognition, traffic management, and autonomous vehicles. The use of object detection has expanded in various aspects of daily life, such as face recognition, traffic management, and autonomous vehicles. To perform object detection, large and complex datasets are required. Therefore, this research addresses what object detection algorithms are suitable for object detection. In this research, i will compare the performance of several algorithms that are popular among young people, such as YOLOv5, YOLOv7, and YOLOv8 models. By conducting several Experiment Results such as Detection Results, Distance Traveled Experiment Results, Confusion Matrix, and Experiment Results on Validation Dataset, I aim to provide insight into the advantages and disadvantages of these algorithms. This comparison will help young researchers choose the most suitable algorithm for their object detection task.

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Author Biographies

Ryan Satria Wijaya, Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam,

Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam,

Santonius Santonius, Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia

Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam,

Anugerah Wibisana, Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia

Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia

Eko Rudiawan Jamzuri, Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia

Jurusan Teknik Elektro, Politeknik Negeri Batam, Batam, Indonesia

Mochamad Ari Bagus Nugroho, Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya, Surabaya

Departemen Teknik Elektro, Politeknik Elektronika Negeri Surabaya, Surabaya

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Published
2024-06-24
How to Cite
Wijaya, R., Santonius, S., Wibisana, A., Jamzuri, E., & Nugroho, M. (2024). Comparative Study of YOLOv5, YOLOv7 and YOLOv8 for Robust Outdoor Detection. Journal of Applied Electrical Engineering, 8(1), 37-43. https://doi.org/10.30871/jaee.v8i1.7207
Issue
Vol 8 No 1 (2024): JAEE, June 2024
Section
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