Published Paper Details:

Traffic congestion is one of the major problems in major cities due to the increase in the number of vehicles. In the current situation, we come across various problems in traffic regulations in India which can be solved with different ideas. Riding a motorcycle without wearing a helmet is a traffic violation that has resulted in an increase in the number of accidents and deaths in India. The existing system monitors traffic violations primarily through CCTV recordings, where the traffic police have to look into the frame where the traffic violation is happening, and zoom into the license plate in case the rider is not wearing a helmet Helmet detection method is a combination of classification and cluster. Helmet detection is an important, yet challenging vision task. It is a critical part of many applications such as traffic surveillance. Our proposed method work is as follows, Pre-processing, Feature Extraction, and classification. In this paper, the survey is done on many methods and technologies used to perform the objectives. Recent research has successfully done this work based on CNN, R-CNN, LBP, HoG, HaaR features, etc. In this research work, a Non-Helmet Rider detection system is built to satisfy the automation of detecting the traffic violation of not wearing helmets and extracting the vehicle�s license plate numbers. The main principle involved is Object Detection using Deep Learning at three levels. The objects detected are a person with a motorcycle at the first level using YOLOv3, a helmet at a second level using YOLOv5, License plate at the last level using YOLOv3. Then the license plate registration number is extracted using OCR (Optical Character Recognition). All these techniques are helpful to detect the stolen vehicle from the database and by using this traffic police officers would know about the stolen vehicle. Using this system, officers can track the stolen vehicle using location reports.