YOLO-Based Real-Time Border Security Surveillance System Using Deep Learning

In today's globalized world traditional methods of securing borders through physical barriers and military presence have proven less efficient. These systems are labor-intensive, and prone to human error especially in difficult terrains. This project utilizes advanced technology to replace traditional armed patrols. By integrating machine learning algorithms with sensors and cameras the system can detect threats like person, vehicle, weapons and drones in real-time and notify authorities through an integrated alert system. Future improvements include deploying the model on IoT devices, for live threat detection and creating a graphical user interface (GUI) for real-time detection. This system aims to enhance border security while reducing the need for physical patrols and minimizing resource consumption.