Improving Students' Residence Hall Security with Machine Learning Techniques

Ensuring student safety in campus dormitories is vital for modern educational institutions. Our innovative Student Residence Hall Security System integrates advanced technologies for comprehensive threat detection. The Facial Recognition module combines Multi-task Cascaded Convolutional Networks (MTCNN) for precise facial feature detection and the FaceNet model for reliable recognition of authorized individuals. This ensures secure access control, allowing only verified residents and staff into dormitories. Additionally, our system uses the YOLOv8 (You Only Look Once) model for Harmful Object Detection, identifying and flagging dangerous items such as weapons or unauthorized electronics. This proactive feature prevents potential security breaches. The Gathering Detection module, powered by the MobileNet Single Shot Detector (SSD) model, monitors crowd behavior, detecting gatherings or unusual activities around residence halls in real time. This supports efficient crowd management and timely responses to potential security threats. The system's adaptability allows deployment across various campus sizes, enhancing security for students, staff, and faculty. Unlike previous research that focused solely on face recognition and detection, our research extends to high-precision identification of gatherings and harmful objects, creating a holistic and proactive security solution.