YOLOv8-Based Helmet and Plate Detection with Bilingual output

Abstract

Road accidents and traffic violations, particularly the non-usage of helmets and unregistered vehicles, remain a serious concern in developing nations. Manual monitoring by traffic police is often inefficient and error-prone. To address this challenge, we propose an Automatic Helmet and License Plate Detection System with Bilingual Output using YOLOv8. The system integrates real-time helmet detection and license plate recognition with bilingual (English and regional language) feedback, ensuring wider accessibility and usability. Leveraging YOLOv8’s advanced object detection capabilities, the model achieves high accuracy and real-time performance.. Experimental results demonstrate that our approach achieves an mAP of over 90% for helmet detection and license plate recognition, with an average inference speed of 35 FPS on GPU. The proposed system can be deployed for intelligent traffic monitoring, law enforcement, and smart city applications.