Deep Learning Based Smart Security and Surveillance System

Abstract

As security concerns climb alongside rapid technological advancements, innovative surveillance solutions are becoming increasingly vital. This project dives into the design and deployment of a Deep Learning-powered Smart Security and Surveillance System, aiming to provide efficient monitoring and proactive threat detection. The system boasts a robust wireless network thanks to the LoRa protocol, enabling seamless connections across diverse environments. This enhanced connectivity significantly improves the chances of early threat detection within a protected area. The system leverages various sensors like PIR detectors, microwave sensors, and magnetometers to pick up on potential threats before they escalate. Furthermore, Wi-Fi modules ensure critical information is processed promptly, eliminating delays that could hinder response times. PTZ cameras with operator control offer a wider viewing radius and improved surveillance capabilities, empowering operators to maintain a watchful eye over a larger area. To make this system even more user-friendly, a special Graphical User Interface (GUI) is built using Flutter, allowing for real-time monitoring and supervision. This project will delve into the system's components, its construction and operation, and ultimately assess its effectiveness in bolstering security and surveillance efforts.