A Survey on Helmet Detection by CNN Algorithm

• Published in: ITM Web of Conferences Vol. 56; p. 5004
• Main Authors: Kapse, Arvind S, Shreevamshi, P, Ravichandra, Reddy, Ranadheer, Reddy, Revanth
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2023
• Subjects: Accidents; Algorithms; Automation; Character recognition; Helmets; Image manipulation; Traffic control; Vehicle identification
• ISSN: 2271-2097; 2431-7578; 2271-2097
• DOI: 10.1051/itmconf/20235605004

Accidents by not wearing helmet infractions are now a big problem for most emerging nations in the modern, changing world. Both the number of vehicles on the road and the number of traffic law offences are growinPly. Not wearing the helmet enforcement has always had a difficult and risky job. Despite the fact that traffic control has evolved into Due to the variety of plate types, various sizes, rotations, and uneven illumination during picture capture, automating the process is a particularly difficult task. The main goal of this project is to properly and efficiently control thehe accidents because of not wearing Helmet. The suggested model incorporates a computer-based camera-based automated system for video recording. In order to identify number plates more quickly and easily, the project offers Automatic Number Plate Recognition (ANPR) approaches as well as additional image-manipulation methods for plate localization and character recognition. The SMS-based module is used to alert the owners of the vehicles about their traffic rule violations after identifying the car number from the number plate. To trace the report, an additional SMS is sent to the Regional Transport Office (RTO).