Advanced International Journal for Research

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AI-Powered Traffic Sign Recognition With Real-Time Weather Advisory

Author(s) Prof. Monika Walde, Tanmay Baghel, Prasanna Navghare, Rushikesh Dabre, Prince Sontakke, Kajal Sonwane
Country India
Abstract This project presents the design and development of an AI-powered web application for the real-time recognition of traffic signs, integrated with a location-based weather advisory system to enhance driver safety. The system leverages a Convolutional Neural Network (CNN) trained on the German Traffic Sign Recognition Benchmark (GTSRB) dataset to accurately classify 43 distinct types of traffic signs from both user-uploaded images and live webcam feeds. The web application, built using Python and the Flask framework, provides a comprehensive user experience, including secure authentication via Google Sign-In, a personal dashboard for viewing prediction history, and a password-protected administrative panel for system oversight.
The system's methodology follows a robust pipeline from data processing to deployment. Input images are subjected to a series of preprocessing steps, including grayscale conversion, histogram equalization, and normalization, to match the format used for training the model. The underlying CNN, trained with extensive data augmentation techniques, demonstrates exceptional performance with a test accuracy exceeding 99%. Furthermore, the application integrates with the OpenWeatherMap API, using the browser's Geolocation API to fetch real-time weather data and provide context-specific driving advice, such as warnings for fog, rain, or snow. The project successfully proves the viability of integrating a high-accuracy AI model into a full-featured, user-centric web application, creating a practical tool for enhancing driver awareness and road safety.
Keywords Traffic Sign Recognition (TSR), Convolutional Neural Network (CNN), Flask, Computer Vision, Real-Time Weather, Google Sign-In, Deep Learning, Advanced Driver-Assistance Systems (ADAS), OpenCV.
Field Engineering
Published In Volume 6, Issue 6, November-December 2025
Published On 2025-12-23
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