AI-Powered Traffic Surveillance: License Plate Recognition with Non-Helmet Detection Using YOLOv8

Helmet detection(HD) is critical for modern intelligent systems to locate and identify various bike riders without helmets. HD is challenging due to several issues, such as the helmet’s shapes, color, and designs, uneven outlines, angle changes, and occlusion. Automatic license plate recognition (ALPR) technology recognizes the optical character on the number plate and locates the bike riders without a helmet by detecting the license plate. This research focuses on identifying the helmet detection of the bike images captured by CCTV in India. This paper presents an HD approach to address the problems as mentioned earlier. The suggested strategy consists of the following steps. First, the You Only Look Once version 8 (YOLOv8) network detects an image of a bike that shows up in an input image. The helmet is next detected within the indicated bike using morphological techniques, and finally, the network is employed to recognize license plates. The simulation results test shows an accuracy rate of 99.34% and character recognition of 98.76% , respectively.