Helmet Detection and Number Plate Recognition using YOLOv3 in Real-Time

Motorcycles have traditionally served as the primary mode of transportation in various countries across the globe. However, there has been a rise in motorcycle-related incidents resulting in harm or fatalities over time. Inadequate head protection remains one of the primary factors leading to lethal outcomes resulting from such incidents. This research endeavours to tackle the issue by designing a system capable of identifying motorcyclists who are not wearing helmets instantly. The proposed system aims to facilitate the use of helmets among motorcycle riders in real-time, helping reduce fatalities due to head injuries, which are a major concern in many countries. Additionally, the system also includes a method for real-time number plate detection and recognition, which has numerous good significances in law enforcement and surveillance. This helps the officers to keep on the people who violate traffic rules and regulations, ensuring their safety as well as that of pedestrians. The system utilizes state-of-the-art deep learning algorithms, such as YOLOv3, to achieve real-time helmet-wearing detection with unparalleled speed and accuracy of 99%.