A Novel Approach for Traffic Sign Detection: A CNN-Based Solution for Real-Time Accuracy
Keywords:
Traffic sign detection, Real-time conditions, CNN, Intelligent transportation systems, Autonomous vehiclesAbstract
The accurate detection of time-dependent traffic signs in real-time conditions is a critical component of intelligent transportation systems and autonomous vehicles. This paper presents a recommendation system to address this challenge by leveraging a specific Convolutional Neural Network (CNN) algorithm tailored for the dynamic nature of traffic sign recognition. Traditional traffic sign detection methods often struggle with variations in lighting, weather, and sign degradation over time, leading to decreased accuracy. Our proposed system overcomes these limitations through a multi-stage process that inputs real-time input from the user and environment and process specific algorithm accordingly. This enables our system to accurately recognize and classify traffic signs under varying lighting conditions, weather scenarios, and even when signs are partially obscured or damaged. We evaluate the performance of our approach using a comprehensive dataset collected from real-world traffic scenarios, demonstrating significant improvements in accuracy compared to existing methods. The results underscore the potential of our solution to enhance the safety and reliability of transportation systems by providing precise and robust traffic sign detection in dynamic, real-time conditions. This research contributes to the broader goal of developing more intelligent and efficient traffic management systems and autonomous vehicles.
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