Helmet Detection and Number Plate Recognition using Machine Learning

Authors

  • Dnyaneshwar Kokare Professor, Department of Computer Engineering, G. H. Raisoni Institute of Engineering and Technology, Pune, India
  • Aaditi Ujwankar Student, Department of Computer Engineering, G. H. Raisoni Institute of Engineering and Technology, Pune, India
  • Alisha Mulla Student, Department of Computer Engineering, G. H. Raisoni Institute of Engineering and Technology, Pune, India
  • Mrunal Kshirsagar Student, Department of Computer Engineering, G. H. Raisoni Institute of Engineering and Technology, Pune, India
  • Apurva Ratnaparkhi Student, Department of Computer Engineering, G. H. Raisoni Institute of Engineering and Technology, Pune, India

Keywords:

OCR, Yolo, Machine Learning, AI

Abstract

The major form of mobility for motorcycles in poor countries has traditionally been the bicycle. Recently, the number of motorcycle accidents has risen. One of the most common causes of motorcycle-related deaths is that the rider is not wearing a helmet. In order to ensure that motorcyclists wear a helmet, traffic police patrol road junctions or review CCTV footage and penalize individuals who are spotted without a protective device. Human intervention and effort are required to make this happen. As a result, this method proposes an automated technique for detecting and obtaining motorcycle number plates from CCTV video footage of riders who are not wearing helmets. Motorcyclists and non-motorcyclists are first categorized by the system. The classification of a motorcyclist's head is based on whether or not he or she wears a helmet. At long last, the OCR algorithm can decipher the number plate of the motorcycle driven by the rider who was not wearing a helmet.

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Published

16-06-2022

Issue

Section

Articles

How to Cite

[1]
D. Kokare, A. Ujwankar, A. Mulla, M. Kshirsagar, and A. Ratnaparkhi, “Helmet Detection and Number Plate Recognition using Machine Learning”, IJRESM, vol. 5, no. 6, pp. 113–115, Jun. 2022, Accessed: Nov. 21, 2024. [Online]. Available: https://journal.ijresm.com/index.php/ijresm/article/view/2169