Automated Hazardous Equipment Identification for Instance Threat Monitoring

SUHAS GOWDA LC; Mr.Selvam

Automated Hazardous Equipment Identification for Instance Threat Monitoring

August 2025

Vol-11, Issue-4

Paper ID: 27405

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Computer Applications

Keywords

Weapon Recognition YOLOv8 Handguns Knives Real-time Detection Object Recognition Deep Learning Image Classification Security Systems Threat Detection Computer Vision Public Safety.

Abstract

The project "Automated Hazardous Equipment Identification for Instance Threat Monitoring" aims to develop a robust and efficient system for identifying weapons, specifically handguns and knives, through the application of advanced Advanced Neural Networks techniques. Implemented using Python as the primary coding language, the project leverages the Flask web framework to deliver an interactive and user-friendly interface, complemented by HTML, CSS, and JavaScript for front-end development. The core of the Recognition mechanism is built upon the YOLOv8 (You Only Look Once version 8) architecture, a state-of-the-art object Recognition model known for its high speed and accuracy. Despite the complexity of the task, the model achieves an overall accuracy of 64%, a notable performance given the challenging nature of weapon Recognition in varied environments. The training dataset comprises approximately 4000 images, focusing exclusively on handguns and knives, ensuring that the model is well-calibrated to recognize these specific threats. This dataset is meticulously curated to include a diverse array of scenarios and perspectives, enhancing the model's ability to generalize across different contexts. The system supports three distinct Recognition modes: static image Recognition, video stream analysis, and real-time Recognition via webcam. This multi-faceted approach ensures flexibility and applicability in various use cases, from security screening and Monitoring to automated threat Recognition systems. Overall, this project represents a significant step forward in the application of Advanced Neural Networks for public safety and security.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	SUHAS GOWDA LC	T JOHN INSTITUTE OF TECHNOLOGY
2	Mr.Selvam	T JOHN INSTITUTE OF TECHNOLOGY

How to Cite

Use the following formats to cite this article in your research.

APA Style

LC, SUHAS GOWDA & Mr.Selvam (2025). Automated Hazardous Equipment Identification for Instance Threat Monitoring. International Journal of Advance Research and Innovative Ideas In Education, 11(4), 3967-3972.

MLA Style

LC, SUHAS GOWDA, and Mr.Selvam. "Automated Hazardous Equipment Identification for Instance Threat Monitoring." International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, 2025, pp. 3967-3972.

IEEE Style

SUHAS GOWDA LC and Mr.Selvam, "Automated Hazardous Equipment Identification for Instance Threat Monitoring," International Journal of Advance Research and Innovative Ideas In Education, vol. 11, no. 4, pp. 3967-3972, 2025.

Vancouver Style

LC SUHAS GOWDA, Mr.Selvam. Automated Hazardous Equipment Identification for Instance Threat Monitoring. International Journal of Advance Research and Innovative Ideas In Education. 2025;11(4):3967-3972.

Harvard Style

LC, SUHAS GOWDA & Mr.Selvam (2025) 'Automated Hazardous Equipment Identification for Instance Threat Monitoring', International Journal of Advance Research and Innovative Ideas In Education, 11(4), pp. 3967-3972.

Chicago Style

LC, SUHAS GOWDA and Mr.Selvam. "Automated Hazardous Equipment Identification for Instance Threat Monitoring." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3967-3972.

Turabian Style

LC, SUHAS GOWDA and Mr.Selvam. "Automated Hazardous Equipment Identification for Instance Threat Monitoring." International Journal of Advance Research and Innovative Ideas In Education 11, no. 4 (2025): 3967-3972.