Efficient Helmet and Number Plate Detection using YOLOv3: A Road Safety Perspective

Object recognition is an important aspect of machine learning and CV tasks, in addition to many real-world uses, including traffic welfare. In this work, we provide a YOLOv3-based trained model for number plate and helmet recognition in photos. Pre-processing the data improved its quality and diversity. We gathered a dataset of annotated photos of helmets and licence plates. We trained our model with a multi-phase program for learning rates using the dataset, then we assessed its efficacy on an independent test collection set. Additionally, we examined its advantages along with disadvantages of the methodology we used also spoken about potential developments that can be done in coming times to raise the model's precision and effectiveness. Our study advances the development of realistic, efficient, along systems for detecting objects that increase traffic welfare.