Integrating an AI Based Vigilance Guard for Army Surveillance

Abstract

Security threats, particularly from terrorists, require advanced and proactive monitoring solutions. Traditional surveillance systems are limited in their ability to detect concealed weapons or assess potential threats based on physiological data. This project proposes the development of a spy robot equipped with temperature sensor, capable of live monitoring and threat detection. The robot can navigate through environments autonomously or via remote control, scanning for metallic objects (potential weapons) and monitoring body temperature to identify anomalies. The robot provides live video feeds and environmental data, while image processing algorithms analyze these feeds to detect suspicious behaviors and potential threats. The integration of these sensors with real-time data transmission enables immediate action, enhancing the overall effectiveness of security operations.

Introduction

Background and Motivation:
Traditional military surveillance methods, relying heavily on human monitoring like CCTV and manual checks, are reactive and prone to errors, especially in high-risk environments. This project proposes an AI-powered vigilance guard system to enhance military security through real-time threat detection, autonomous navigation, and integration of sensors such as thermal imaging and metal detectors. The goal is to improve 24/7 monitoring and operational effectiveness.

Problem Statement:
Current surveillance systems suffer from limited accuracy, delayed response, inefficient resource use, cybersecurity vulnerabilities, and restricted coverage. These issues hinder effective threat detection and defense in modern complex military scenarios.

Objectives:
The project aims to develop an AI-based system to:

• Enhance real-time threat detection accuracy

• Automate continuous surveillance to reduce human fatigue

• Improve situational awareness through data analytics

• Optimize deployment of personnel and equipment

• Integrate smart sensors and AI-driven drones

• Minimize false alarms

• Strengthen cybersecurity protections

Related Work:
Research has shown AI’s effectiveness in object detection, facial recognition, and anomaly detection in military contexts. AI-powered drones assist reconnaissance but face challenges like overfitting and limited real-time processing. Machine learning aids threat prediction but can generate false alerts. Cybersecurity AI helps defend against cyber-attacks but military systems remain high-risk targets.

Proposed System:
The system architecture includes AI-powered threat detection using machine learning and computer vision, IoT sensors, autonomous drones, and automated alerting controlled by an ESP32 microcontroller. Key components include temperature and gas sensors, robotic mechanisms, and remote IoT monitoring. The system offers real-time detection, autonomous surveillance, predictive threat analysis, and enhanced cybersecurity.

Expected Outcomes:
The system aims for about 96% threat detection accuracy, 24/7 automated monitoring, optimized resource allocation, and stronger cybersecurity. It will continuously improve through adaptive intelligence and integration of drones and smart sensors to create a comprehensive defense network.

Results and Discussion:
Implementation improves detection accuracy, reduces response time, and enhances operational efficiency while minimizing human fatigue. AI analytics enable proactive threat management and better resource use. Challenges include cybersecurity risks, ethical issues, and occasional false positives/negatives requiring human oversight. Overall, AI vigilance guards significantly enhance military surveillance capabilities and strategic defense, though ongoing improvements are necessary.

Conclusion

Integrating an AI-based vigilance guard for army surveillance enhances national security by providing real-time monitoring, rapid threat detection, and efficient decision-making. AI-powered surveillance reduces human limitations such as fatigue and reaction time, ensuring continuous and accurate observation of critical areas. By leveraging advanced technologies like computer vision, machine learning, and sensor integration, the system can identify potential threats, analyze patterns, and provide predictive intelligence. This integration not only strengthens border security but also minimizes risks for personnel, allowing them to focus on strategic operations. However, the implementation requires robust cybersecurity measures, ethical considerations, and ongoing improvements to optimize performance. In conclusion, an AI-based vigilance guard is a game-changer in modern military surveillance, significantly enhancing operational efficiency and national defense capabilities For future work, we would like to modify the structure of the CNN algorithm to deal with limited training data. We are also interested in investigating the efficacy of our intrusion detection system on different robotic platforms, such as unmanned aerial vehicles, whose dynamics are reasonably faster and more complex compared to a ground robot. Under the umbrella of deep learning (supervised and unsupervised) systems, we are also keen to study the relative merits of our CNN intrusion detection algorithm with respect to similar detection techniques such as evolving type-2 fuzzy systems that can accommodate the footprint-of-uncertainties

References

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Copyright

Copyright © 2025 K Dharmendra, M Bharath, Rothish kumar D, Mr. K. Balaji. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.