Deep-learning based helmet violation detection system
Namit Kharade, Saiel Mane, Jitender Raghav, Neha Alle, Amrut Khatavkar, Geeta S. Navale
Abstract
The detection of helmeted and non-helmeted motorcyclists is necessary to preserve the safety of riders on the road. Helmets are meant to keep the driver’s head safe in the case of a collision. If a biker does not wear a helmet and is involved in an accident, it might result in death. Most traffic and safety regulations violations are now identified by analysing traffic recordings acquired by security cameras. The focus of this paper is to provide a technique for detecting motorcyclists who are not wearing a helmet. In this research, we use a deep learning algorithm to develop a strategy for automatically detecting helmeted and non-helmeted motorcyclists. Motorcycle riders are recognised in this study using the YOLOv4 model, which is an incremental version of YOLO model and is a cutting-edge object detection algorithm. When compared to existing CNN based algorithms, the proposed model shows good performance on traffic videos.