Authors : Dr. Tabasum Guledgudd; Tanveer Khatib; K. Rama; Kannika Raikar; Bhavani K Badli; Kanivihalli Jyothi

Volume/Issue : Volume 11 - 2026, Issue 5 - May

Google Scholar : https://tinyurl.com/2vrk8ubp

Scribd : https://tinyurl.com/3tp9feue

DOI : https://doi.org/10.38124/ijisrt/26May1772

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The growing need for intelligent and autonomous security systems in defense zones, industrial areas, and public spaces has accelerated the development of advanced surveillance technologies. Traditional systems relying on fixed CCTV cameras and manual human monitoring suffer from critical limitations including restricted coverage, operator fatigue, blind spots, and delayed threat response. This paper presents a Computer Vision-Driven Smart Patrol Robot for Automated Defence Monitoring that integrates autonomous robotics with advanced artificial intelligence techniques. The proposed system employs an onboard camera to capture real-time video while patrolling a designated area, and processes the footage using Convolutional Neural Networks (CNN) and the YOLO (You Only Look Once) object detection algorithm to identify intrusions, weapons, and abnormal human activities. Upon detection of a threat, the system immediately generates alerts for the concerned authorities. The framework is designed to reduce dependency on manual surveillance, enhance detection accuracy, and ensure continuous uninterrupted monitoring. The proposed solution is scalable, cost-effective, and deployable in defence zones, industrial facilities, and public environments, representing a significant advancement in intelligent autonomous surveillance technology.

Keywords : Computer Vision, YOLO, Smart Patrol Robot, Autonomous Surveillance, Object Detection, CNN, Defence Monitoring, Threat Detection.

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