CATTLE DETECTION AND MISHAP AVOIDANCE SYSTEM USING YOLO v5 ALGORITHM

Authors

  • Vinayak Sutar Department of Electronics and Telecommunication Engineering, DKTE Society’s Textile and Engineering Institute, Rajwada, Ichalkaranji, India
  • Kshama Kulhalli Dr. D.Y. Patil College of Engineering & Technology, Kolhapur, India

DOI:

https://doi.org/10.58973/CTRJ.22115

Keywords:

Object detection, Road Safety, CNN, YOLO, , Mishap Avoidance

Abstract

Aim: We propose a robust vehicular cattle detection model using YOLO version 5 for vehicles to avoid mishap on Indian roads.

Results: The research was conducted with the help of training and validation on-road cattle image dataset and tested the model for various epoch values.

Conclusion: The model has predicted 82% to 85% of true positive result with 90.5% accuracy and fruitful test results observed on real world video samples.

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Published

2022-10-05

How to Cite

Sutar , V., & Kulhalli, K. (2022). CATTLE DETECTION AND MISHAP AVOIDANCE SYSTEM USING YOLO v5 ALGORITHM. Computing Technology Research Journal, 1(1), 5–11. https://doi.org/10.58973/CTRJ.22115

Issue

Vol. 1 No. 1 (2022): October: Computing Technology Research Journal

Section

Short Communication

License

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