Comparison of Transfer Learning Techniques for Object Detection

Authors

  • Ayush Majumdar School of Computer Engineering and Technology, Dr.Vishwanath Karad MIT World Peace University, Pune
  • Siddharth Sarma School of Computer Engineering and Technology, Dr.Vishwanath Karad MIT World Peace University, Pune
  • Sarvesh Satone School of Computer Engineering and Technology, Dr.Vishwanath Karad MIT World Peace University, Pune
  • Atharv Mankar School of Computer Engineering and Technology, Dr.Vishwanath Karad MIT World Peace University, Pune
  • Bhavana Tiple School of Computer Engineering and Technology, Dr.Vishwanath Karad MIT World Peace University, Pune
  • Madhura Phatak School of Computer Engineering and Technology, Dr.Vishwanath Karad MIT World Peace University, Pune

Keywords:

Object Detection, YOLOv5, Transfer Learning, Deep Learning

Abstract

The YOLO series of models has been the industry standard for precise and practical object identification since 2015. Since then, YOLO models have been improved for faster and more accurate detection as well as meeting a variety  of requirements      in real-time environments and multiple pertinent scenarios. The first YOLO model introduced the concept of tackling object detection by allowing a neural network to predict bounding boxes and class probabilities in one evaluation. A novel dataset of Indian roads and vehicles has been created using a highly customised dataset of 3k photos obtained from various sources to compare and analyse YOLO models such as YOLOv5, YOLOv6, YOLOv7, and YOLOR in-depth in this paper. The study’s findings reveal that, in a similar testing environment, YOLOv5 performs better than the competition, making it the most accurate YOLO Model to date.

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Published

01.07.2023

How to Cite

Majumdar, A. ., Sarma, S. ., Satone, S. ., Mankar, A. ., Tiple, B. ., & Phatak, M. . (2023). Comparison of Transfer Learning Techniques for Object Detection. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 243–252. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2950

Issue

Vol. 11 No. 7s (2023): Advancements in Machine Learning for Computer Science and Decision Support Systems

Section

Research Article

License

Copyright (c) 2023 Ayush Majumdar, Siddharth Sarma, Sarvesh Satone, Atharv Mankar, Bhavana Tiple, Madhura Phatak

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