Citation

Reddy pasam J, Tatikonda A, Sai vemulapalli P, Sai Sreeram N, Velagala A, Rustum R. J. Eng. Sci. (Riyadh) 2022; 13(5): 177-185.

Copyright

(Copyright © 2022, King Saud University)

DOI

10.15433.JES.2022.V13I5.43P.23

PMID

unavailable

Abstract

Classification and clustering are used to identify helmets. Detecting the presence of a helmet is an important yet difficult visual job. When it comes to applications such as traffic surveillance, this is an essential component. Pre- processing, Feature Extraction, and Classification are the steps we propose to do. We use traffic surveillance photographs to explain our ideas. When all is said and done, our algorithm will determine whether or not the subject is donning a helmet. Our method is superior to existing algorithms in terms of both resilience and effectiveness. A CNN model was created for the purpose of detecting HELMETS and license plates in various photos as part of this research. For the purpose of picture recognition, this form of the system makes use of previously acquired photographs. Number plate extraction, character splitting, and template matching are the first steps in the recognition process. The program must be able to handle number plates fast and effectively in a variety of environmental situations utilizing the CNN model. CNN and the detection of license plates are two of the most commonly used keywords.

Language: en