Comparing Object Detection Models for Public Safety

The safety and security of individuals in crowded public places heavily rely on the detection of dangerous objects. Incorporating advanced technologies, like artificial intelligence, can significantly enhance the identification process, allowing security personnel to swiftly and effectively respond to potential threats. This research paper presents a comprehensive comparative analysis of two widely used object detection models, namely You Only Look Once (YOLO) and Detectron, in the context of baggage screening in public areas such as malls and hotels. This study aims to provide insightful recommendations for improving current detection systems by evaluating their performance and effectiveness. Ensuring public safety remains a paramount concern, and this research contributes to advancing the capabilities of existing security measures, ultimately enhancing public safety in crowded public places.