Authors : Shourya Yadav; Yusuf Rehan; Shreya Arya; Shreya Jain

Volume/Issue : Volume 11 - 2026, Issue 2 - February

Google Scholar : https://tinyurl.com/4v64ycs3

Scribd : https://tinyurl.com/bdfp3xe5

DOI : https://doi.org/10.38124/ijisrt/26feb1485

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 rising traffic and the inability to maintain an oversight manually have augmented the issuesin themanagement of traffic in urban areas. Traditional manual monitoring systems, as far as exposing infractions on the fly is concerned, tend to be inefficient and are more susceptible to mistakes. The proposal presented in this paper is for an AI-based traffic violation detection system known as VisionSafeX that utilizes the ability of the artificial intelligence known as Generative AI to improve the resolution of low-resolution surveillance video in addition to the use of the MERN stack to integrate the system. The system automatically detects the violations with the help of CNN-based model, which processes the frames. Generative AI adds more precision to the model since it is able to recreate missing frames. The experiments and research that was undertaken have demonstrated good performance in different real life situations. The dashboard is provided through a MERN based dashboard that is available on-demand. The paper also discusses deployment strategies, security considerations, scalability, and practical im-plementation guidelines for city-wide traffic networks.

Keywords : Traffic Violation Detection, MERN Stack, Gen-erative AI, Deep Learning, Automation, Video Enhancement, Real-Time Monitoring

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