CNN Based Animals Recognition using Advanced YOLO V5 and Darknet

Authors

  • J. K. Vishwas Student, Department Electronics & Communication Engineering, East West Institute of Technology, Bangalore, India
  • S. M. Prajwal Raj Student, Department Electronics & Communication Engineering, East West Institute of Technology, Bangalore, India
  • Bhagya Professor, Department Electronics & Communication Engineering, East West Institute of Technology, Bangalore, India
  • M. Anand Professor, Department Electronics & Communication Engineering, East West Institute of Technology, Bangalore, India
  • S. Puneeth Student, Department Electronics & Communication Engineering, East West Institute of Technology, Bangalore, India
  • P. B. Prajwal Student, Department Electronics & Communication Engineering, East West Institute of Technology, Bangalore, India

Keywords:

YOLO V5, CNN, OpenCV, Database image processing, Darknet

Abstract

The most of common method of identifying an animal for a human was based on his knowledge what he had acquired yet still the identification of few animals is still a tremendous task. In order to overcome this problem, we make use of advanced technology like computer vision along with YOLO V5 algorithms for the better performance and analysis of animal recognition. A sample input will be fed which will be analyzed using the YOLO algorithms, animal will be recognized and using the pretrained dataset and provide the right output.

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Published

24-06-2022

Issue

Vol. 5 No. 6 (2022)

Section

Articles

License

Copyright (c) 2022 J. K. Vishwas, S. M. Prajwal Raj, Bhagya, M. Anand, S. Puneeth, P. B. Prajwal

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
J. K. Vishwas, S. M. P. Raj, Bhagya, M. Anand, S. Puneeth, and P. B. Prajwal, “CNN Based Animals Recognition using Advanced YOLO V5 and Darknet”, IJRESM, vol. 5, no. 6, pp. 229–231, Jun. 2022, Accessed: Nov. 21, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2206