Real-Time Traffic Zone Analysis Using CNNs for Enhanced Road Safety
Journal Title: International Journal for Modern Trends in Science and Technology - Year 2025, Vol 11, Issue 06
Abstract
The combination of deep learning and real-time notification systems has changed the way we monitor traffic and analyze road conditions in the last few years. The goal of this project is to create a smart Traffic Prediction System that uses deep learning to sort and find different road situations, such as heavy traffic, foggy weather, cracked roads, and the reverse of those conditions. The system can accurately classify images into several classes by using high-performance transfer learning models like VGG16, ResNet50, or InceptionV3. The trained model can look at pictures taken by roadside or surveillance cameras and find several objects or conditions at the same time. This gives a full picture of what is happening on the road right now. Python is used to develop the backend of the system, including frameworks like TensorFlow/Keras for training models and Flask for making the web API. HTML and CSS are used to build the front end, which makes it easy for users to submit images or add live feeds. After an image is processed, the model uses a Telegram bot API to provide real-time notifications to the authorities or users who need to know about certain conditions. This integration makes sure that important road events like accidents caused by traffic jams or fog that makes it hard to see are responded to quickly.This complete solution shows how deep learning can be used in smart city infrastructure to make roads safer and traffic flow better. The modular design of the system makes it easy to add more object identification duties and connect it to camera networks that use the Internet of Things. The suggested technology is a basic framework for future intelligent transportation systems since it can transmit rapid alerts and give reliable reports on road conditions.
Authors and Affiliations
Nageswara Rao Sikha & Ananda Babu Seendra
Keywords
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- EP ID EP769409
- DOI https://doi.org/10.5281/zenodo.15666067
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