AlertEye: Real-time AI-Driven Incident Detection using Yolov11 and GNN Model

Singh, Shashank; Gaikwad, Mayuresh; Shinde, Sujal; Shewale, Tanvi; Sangit, Pranita

doi:https://doi.org/10.55041/ijsmt.v2i4.312

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ALERTEYE: REAL-TIME AI-DRIVEN INCIDENT DETECTION USING YOLOV11 AND GNN MODEL

AUTHORS:

Shashank Singh

Mayuresh Gaikwad

Sujal Shinde

Tanvi Shewale

Pranita Sangit

Mentor

Affiliation

Department of Computer Engineering K.C. College of Engineering, Management Studies and Research Thane, India

DOI: 10.55041/ijsmt.v2i4.312

CC BY 4.0 License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abstract

The increasing complexity of urban environments presents significant challenges for public safety, particularly regarding the delayed detection of road accidents and fire outbreaks. Conventional surveillance systems are primarily reactive and rely on continuous human monitoring, which is prone to fatigue and error. This paper presents "AlertEye," an automated real-time detection and emergency notification system. By leveraging the YOLOv11 deep learning architecture, the system achieves high-speed object detection from live camera feeds. When a hazard is identified with high confidence, a Flask-based backend utilizes Firebase Cloud Messaging (FCM) to deliver instantaneous push notifications to a mobile application. Experimental results demonstrate that the system significantly reduces response latency, offering a proactive solution for emergency intervention.

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This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.

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