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DANGEROUS OBJECT DETECTION IN PUBLIC SPACES FOR SAFETY ASSURANCE
In recent years, ensuring safety in public environments has become a critical challenge due to the rising number of weapon-related incidents. Traditional surveillance systems such as CCTV rely heavily on human monitoring, which is often inefficient due to fatigue, distraction, and delayed response. This project presents an intelligent deep learning-based system for dangerous object detection in public spaces to enhance security and reduce dependency on manual surveillance. The proposed system utilizes advanced object detection algorithms such as YOLO, Faster R-CNN, and SSD MobileNet to identify weapons like guns and knives in real-time from video streams. The system is trained on a diverse dataset consisting of weapon and non-weapon images, including confusion objects to reduce false positives. Image preprocessing techniques such as normalization, augmentation, and enhancement are applied to improve detection performance in challenging conditions like low lighting and crowded environments. The model evaluates performance using precision, recall, F1-score, and mean average precision metrics, ensuring accurate detection. Upon identifying a dangerous object, the system generates immediate alerts, allowing authorities to take timely action. This automated approach improves surveillance efficiency, reduces human workload, and enhances response time. The system is scalable, cost-effective, and suitable for deployment in areas such as airports, railway stations, malls, and educational institutions. Overall, the proposed solution contributes significantly to public safety by providing a reliable, real-time, and intelligent surveillance mechanism
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