AI-Driven Smart Weapon Detection and Public Safety Surveillance System Using YOLOv8 and Deep Learning

This research paper is about a system that uses Artificial Intelligence to detect weapons on time. The system uses a model called YOLOv8 to look at video streams from CCTV cameras and find weapons like pistols, rifles and knives. The YOLOv8 model helps the system find weapons quickly and accurately in areas. When the system finds a weapon it sends an alert to security personnel. This helps them respond faster. Makes the whole system more effective. The system has advantages over traditional surveillance systems. These include: ·        Detecting weapons on time ·        Being very accurate ·        Helping security personnel respond faster ·        Automatically sending alerts ·        Not needing humans to watch the cameras all the time ·        Being good at monitoring surveillance video ·        Helping to keep people safe ·        Being easy to add to existing CCTV systems The Artificial Intelligence system was tested and it worked well. It could detect weapons in video streams. The system uses computer hardware to make it work faster. However the system is not perfect. It can have trouble detecting weapons in light. It also needs a lot of computer power to work. The system needs a dataset to learn from so it can get better at detecting weapons and YOLOv8 model. In the future the Artificial Intelligence system can be improved. It can use technologies like imaging cameras. The system can also be used in cities to help with security and detect weapons. Overall the research shows that using Artificial Intelligence and YOLOv8 model to detect weapons can make a difference in keeping people safe. The system is a solution, for making security systems.

[1]   Joseph Redmon, Santosh Divvala, Ross Girshick and Ali Farhadi wrote a paper called "You Look at Real-Time Object Detection". They presented YOLO at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition in 2016 on pages 779 to 788. YOLO is a system that helps detect objects in time. It really helps detect objects in time. [2]   D. Deshpande, M. Jain, A. Jajoo, D. Kadam H. Kadam and A. Kashyap worked on a project called "Next-Gen Security: YOLOv8 for Real-Time Weapon Detection". They presented YOLOv8 at the 2023 7th International Conference on I-SMAC, which was organized by IEEE. YOLOv8 helps with real-time weapon detection. This project used YOLOv8 for detection. [3]  P. P. Khin and N. M. Htaik did a study called "Gun Detection: A Comparative Study of RetinaNet, EfficientDet and YOLOv8 on Custom Dataset". They presented YOLOv8 at the 2024 IEEE Conference, on Computer Applications, which was organized by IEEE. The study compared YOLOv8 with object detection systems. YOLOv8 was one of the systems they studied with. [4]  G. K. Verma and A. Dhillon worked on a project called "A Hand-Held Gun Detection Using Faster R-CNN Deep Learning". They presented gun detection at the International Conference on Computer and Communication Technology in 2017 on pages 84 to 88. Gun detection is an application of object detection systems. This project detected guns. [5]   M. Grega, A. Matiolanski, P. Guzik and M. Leszczuk wrote a paper called "Automated Detection of Firearms and Knives in a CCTV Image". They published the paper in the journal Sensors in 2016 volume 16 issue 1 on page 47. The paper discussed automated detection of firearms and knives. This paper is about detection. [6]   S. Sarkar, S. Ahire, S. Rahate, R. Barde, R. Agrawal and S. Sorte worked on a project called "Design of Weapon Detection System". They presented weapon detection at the 2022 3rd International Conference on Electronics and Sustainable Communication Systems which was organized by IEEE. Weapon detection systems are critical for security applications. This project designed a weapon detection system. [7]   H. Jain, A. Vikram, M. Mohana A. Kashyap and A. Jain worked on a project called "Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications". They presented weapon detection at the 2020 International Conference on Electronics and Sustainable Communication Systems which was organized by IEEE. Artificial intelligence and deep learning are used for weapon detection. This project used AI and deep learning. [8]   R. Reddy, K. Gyan Vallabh and Sai Sharan worked on a project called "Multiclass Weapon Detection using Multi Contrast Convolutional Neural Networks and Faster Region-Based Convolutional Neural Networks". They presented weapon detection at the 2021 2nd International Conference for Emerging Technology which was organized by IEEE. Multiclass weapon detection is a task. This project detected classes of weapons. [9]   J. L. S. González, C. Zaccaro, J. A. Álvarez-García, L. M. S. Morillo and F. S. Caparrini wrote a paper called "Real-Time Gun Detection in CCTV: An Open Problem". They published the paper in the journal Neural Networks in 2020 volume 132 on pages 297 to 308. Real-time gun detection is a problem. This paper discussed real-time gun detection. [10]   D. K. Jain wrote a paper called "An Evaluation of Deep Learning-Based Object Detection Strategies for Threat Object Detection in Baggage Security Imagery". They published the paper in the journal Pattern Recognition Letters in 2019 volume 120 on pages 112 to 119. The paper evaluated learning-based object detection strategies. This paper evaluated strategies.