Article: Weapon detection technologies for surveillance under different YoloV8 models on primary data: smart city based approach for safe society Journal: International Journal of System of Systems Engineering (IJSSE) 2026 Vol.16 No.2 pp.239 - 266 Abstract: This comparison between the yolov8s.pt and yolov8x.pt YOLOv8 models is very important for real-time applications, particularly for object recognition and surveillance. Based on the results, the 95% precision and recall of the yolov8s.pt model, together with its 96% mean average precision, demonstrate the model's usefulness in situations requiring precise and quick object recognition. This model has potential applications in a variety of security systems, supporting security protocols in high-risk areas such as airports, public areas, and high-security enterprises by assisting in the quick identification of possible threats in real-time surveillance data. Conversely, the yolov8x.pt model's better performance - which includes an astounding 98% precision and 99% mean average precision -highlights its effectiveness in demanding real-time applications that need exacting accuracy. Because of its complex capabilities, the model is a great fit for use in cutting-edge applications that require quick and accurate object recognition, such as autonomous driving technologies and sophisticated surveillance systems. Inderscience Publishers - linking academia, business and industry through research

Title: Weapon detection technologies for surveillance under different YoloV8 models on primary data: smart city based approach for safe society

Authors: Rohit Rastogi; Yati Varshney; Jagrati Sharma

Addresses: Department of CSE, ABES Engineering College Ghaziabad, U.P., 201009, India ' Department of CSE, ABES Engineering College Ghaziabad, U.P., 201009, India ' Department of CSE, ABES Engineering College Ghaziabad, U.P., 201009, India

Abstract: This comparison between the yolov8s.pt and yolov8x.pt YOLOv8 models is very important for real-time applications, particularly for object recognition and surveillance. Based on the results, the 95% precision and recall of the yolov8s.pt model, together with its 96% mean average precision, demonstrate the model's usefulness in situations requiring precise and quick object recognition. This model has potential applications in a variety of security systems, supporting security protocols in high-risk areas such as airports, public areas, and high-security enterprises by assisting in the quick identification of possible threats in real-time surveillance data. Conversely, the yolov8x.pt model's better performance - which includes an astounding 98% precision and 99% mean average precision -highlights its effectiveness in demanding real-time applications that need exacting accuracy. Because of its complex capabilities, the model is a great fit for use in cutting-edge applications that require quick and accurate object recognition, such as autonomous driving technologies and sophisticated surveillance systems.

Keywords: CNN; convolutional neural network; down-sampling; optimisation; weapons; detection; surveillance; object detection; thermal imaging; wave scanning; security infrastructure.

DOI: 10.1504/IJSSE.2026.153654

International Journal of System of Systems Engineering, 2026 Vol.16 No.2, pp.239 - 266

Received: 05 Nov 2023
Accepted: 22 Mar 2024
Published online: 21 May 2026 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article

Title: Weapon detection technologies for surveillance under different YoloV8 models on primary data: smart city based approach for safe society

Keep up-to-date