International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 03 Issue: 03 | Mar-2016 www.irjet.net p-ISSN: 2395-0072 © 2016, IRJET ISO 9001:2008 Certified Journal Page 589 SMART HELMET - INTELLIGENT SAFETY FOR MOTORCYCLIST USING RASPBERRY PI AND OPEN CV Shabrin 1 , Bhagyashree Jagadish Nikharge 2 , Maithri M Poojary 3 , T Pooja 4 , Sadhana B 5 1 Student, Information Science and Engineering, Canara Engineering College, Karnataka, India 2 Student, Information Science and Engineering, Canara Engineering College, Karnataka, India 3 Student, Information Science and Engineering, Canara Engineering College, Karnataka, India 4 Student, Information Science and Engineering, Canara Engineering College, Karnataka, India 5 Asst Prof, Information Science and Engineering, Canara Engineering College, Karnataka, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Smart Helmet - Intelligent Safety Helmet for Motorcyclist is a project undertaken to increase the rate of road safety among motorcyclists. The idea is obtained after knowing that there is increased number of fatal road accidents over the years. Through the study identified, it is analysed that the helmets used is not in safety features such as not wearing a helmet string and not use the appropriate size. Therefore, this project is designed to introduce safety systems for the motorcyclist to wear the helmet properly. With the use of Image processing unit using Raspberry Pi and Open Cv , the motorcycle can move if there is helmet pound wearing, in accordance with the project title Smart Helmet - Intelligent Safety for Motorcyclist using Raspberry Pi and Open Cv. Safety system applied in this project meet the characteristics of a perfect rider and the application should be highlighted. The project is expected to improve safety and reduce accidents, especially fatal to the motorcyclist. Key Words: Safety, Standard Detection, Motor Ignition, Raspberry Pi, Open Cv. 1. INTRODUCTION Two-wheelers, the mode of transport most Indians use, continue to be the most vulnerable to accidents. Indian roads were at their deadliest in 2014 claiming more than 16 lives every hour on average. Over 1.41 lakh people died in crashes, 3% more than the number of fatalities in 2013. Accidents involving two-wheelers and accounted for nearly half of the lives lost in road crashes. While 13,787 two- wheeler drivers were killed in crashes, 23,529 other people were killed in accidents involving these vehicles, while close to 1.4 lakh people were left injured in them. The top five states - Uttar Pradesh, Tamil Nadu, Maharashtra, Karnataka and Rajasthan - accounted for over 40% of the fatalities. Among 53 mega cities, Delhi registered the highest number of fatalities at 2,199 and Chennai recorded 1,046 such deaths. Bhopal and Jaipur ranked third and fourth with the city roads claiming 1,015 and 844 lives respectively[1, 2]. A motorcycle’s helmet is a type of protective headgear used by the motorcyclist. The main purpose is for safety, which is to protect the rider's head from the impact during an accident. )t protects the rider’s head as the helmet provides ventilation system. Speeding and not wearing a helmet are the main reasons of fatalities and injuries. Here we are implementing a model which uses DC Motor, Relay and Raspberry Pi which in real time system is related to the ignition system of the Motorcycle. 2. RELATED WORK The system automatically detects motorcycle riders and determines that they are wearing safety helmets or not. The system extracts moving objects and classifies them as a motorcycle or other moving objects based on features extracted from their region properties using K-Nearest Neighbour (KNN) classifier. The heads of the riders on the recognized motorcycle are then counted and segmented based on projection profiling. The system classifies the head as wearing a helmet or not using KNN based on features derived from 4 sections of segmented head region. Experiment results show an average correct detection rate for near lane, far lane, and both lanes as 84%, 68%, and 74%, respectively [3]. The helmet is the main safety equipment of motorcyclists, but many drivers do not use it. If an motorcyclist is without helmet an accident can be fatal. This paper aims to explain and illustrate an automatic method for motorcycles detection and classification on public roads and a system for automatic detection of motorcyclists without helmet. For this, a hybrid descriptor for features extraction is