A Next-Generation Platform for Law Enforcement Vehicle Detection

This paper presents an in-depth exploration of a novel platform designed to modernize police detection of wanted vehicles, situated at the intersection of Flask, a versatile web framework, and MongoDB, a leading NoSQL database platform. Our objective is to streamline vehicle detection processes while investigating MongoDB's potential in AI-driven applications, particularly in law enforcement. Through thorough analysis, rigorous experimentation, and insightful observations, we expound MongoDB's role in augmenting AI capabilities, focusing on vehicle detection and surveillance. The platform integrates two AI models: YOLOv8, renowned for accurate object detection including license plates, and EasyOCR, specialized in precise text extraction from images. By detailing the methodologies and contributions of these models, we aim to inspire advancements in law enforcement through innovative technology integration. The accuracy obtained from this system reaches 98%. Our research delves into the architecture and development methodologies of the platform, highlighting the synergies between Flask and MongoDB. Additionally, we discuss the practical implications of MongoDB's flexible document-oriented structure, enabling seamless integration with AI models for efficient data processing. Through comprehensive experimentation, we demonstrate the platform's efficacy in enhancing law enforcement efforts, offering insights into its potential applications in surveillance and crime prevention.