Helmet Detection and License Plate Extraction for Two-Wheeler Offenders

Helmet Detection and License Plate Extraction for Two-Wheeler Offenders is a critical issue in many countries, particularly in developing countries where two-wheeler riders make up a significant portion of the population. In the case of an accident, not wearing a helmet while operating a motorbike or scooter might cause serious brain injuries or even death [1]. To address this issue, several technologies and systems have been developed to encourage two-wheeler riders to wear helmets [2]. Using YOLO v8 on Google Colab, this project aims to create a system for identifying two-wheeler helmet offenders and extracting license plate data. The system accurately recognizes and extracts relevant data from images and videos using object-detection algorithms. The system was evaluated for both helmet identification and license plate extraction using a variety of measures, including accuracy, precision, and recall. The system's success in policing traffic regulations and improving road safety demonstrates its future work could focus on expanding the dataset to further improve the system's accuracy and robustness.