Current Volume 10
Abstract
Traffic signal detection is a crucial component of intelligent transportation systems (ITS), enabling safer and more efficient road usage. However, accurately identifying traffic signals under adverse environmental conditions such as fog, rain, and low-light environments remains a significant challenge. Traditional image processing techniques often fail to provide reliable results due to poor visibility and environmental noise. This project presents a Convolutional Neural Network (CNN)-based approach for robust traffic signal detection in low-visibility scenarios. The proposed system utilizes a deep learning model trained on a diverse dataset of traffic signal images collected under various weather conditions. Preprocessing techniques such as image resizing, normalization, and contrast enhancement are applied to improve feature extraction and model performance. The model is developed using Python with TensorFlow and scikit-learn, and evaluated based on performance metrics including accuracy, precision, and recall. The system is designed to classify traffic signals into different categories such as red, yellow, and green with high reliability. A simple graphical user interface (GUI) is also implemented using Tkinter to facilitate user interaction. The expected outcome of the project is an efficient and accurate traffic signal detection system capable of operating under challenging environmental conditions. This system can be further integrated into autonomous vehicles, driver assistance systems, and smart traffic management solutions to enhance road safety and automation.
Citations
IRE Journals:
Rohan Thanage, Onkar Shete, Utkarsh Tope, Dr. D. V. Gore "CNN-Based Traffic Signal Detection for Low Visibility Scenarios" Iconic Research And Engineering Journals Volume 9 Issue 12 2026 Page 2695-2700 https://doi.org/10.64388/IREV9I12-1719091
IEEE:
Rohan Thanage, Onkar Shete, Utkarsh Tope, Dr. D. V. Gore
"CNN-Based Traffic Signal Detection for Low Visibility Scenarios" Iconic Research And Engineering Journals, 9(12) https://doi.org/10.64388/IREV9I12-1719091
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