Deep Learning-Based Object Detection System for Crowd Monitoring in High-Security Areas

In order to maintain crowd control and assure safety, contemporary security systems require effective monitoring tools for sensitive and high-traffic locations. In particular, headcount estimate in sensitive areas like high-security buildings, crowded areas is the subject of this research’s novel using deep learning algorithms for real-time object identification in surveillance settings. The main goal is to create a reliable system that can count the number of people in a certain area, recognize their presence, and sound an alarm when the number of people in the crowd rises over pre-established levels. YOLO (You Only Look Once), RetinaNet, SSD (Single Shot MultiBox Detector), and RCNN (Region-based Convolutional Neural Networks) are four of the cutting-edge deep learning models that are applied in this study of object recognition. In order to identify the best method for precise and effective headcount estimation, the study comprises a comparison review of various models. And of all the models examined, YOLO consistently showed the greatest results for precision, recall, and average precision, making it the most efficient model for this specific project.