Paper #3

Understanding the Traffic Sign through a Deep CNN Architecture

Naima Islam, Sajeeb Kumar Ray, Md Mynoddin, Md. Tofael Ahmed, Md. Zahid Hasan, Sheikh Mohammad Jawad and Md. Anwar Hossain

Abstract: Accurate traffic sign classification is essential for intelligent transportation systems and automated vehicles to ensure safety and navigation efficiency. Our research showcases a specialized CNN model focused on achieving precision in traffic sign detection, utilizing the GTSRB dataset for training. The model incorporates advanced methodologies, including skip connections and bilinear interpolation, to mitigate issues like image noise and low resolution. Skip connections preserve vital features across layers to prevent information loss during training, while bilinear interpolation enhances image clarity for improved recognition under various real-world conditions. The architecture consists of multiple convolutional and pooling layers optimized for extracting and maintaining detailed features crucial for accurate classification. With these improvements, the suggested model achieves a remarkable accuracy rate of 99.78%, indicating its competence in identifying traffic signs. Additionally, the model was evaluated on the TT-100K dataset and the Traffic Sign Classification dataset, with accuracies of 99.78% and 98.86%, respectively. This precision showcases the strength and flexibility of the model, confirming its reliability in the development of advanced transportation technologies, while highlighting its practical usefulness in real-world traffic monitoring systems. By enhancing the reliability of traffic sign recognition systems, this research addresses current shortcomings and significantly improves the safety of automated vehicles and road users. The results emphasize the potential of innovative computer vision techniques in traffic sign classification, fostering progress in intelligent transportation systems and automated driving technologies. This investigation represents a crucial advancement toward bridging theoretical research and practical implementation, thereby improving the reliability and safety of contemporary transportation networks.

Keywords: Computer Vision; Convolutional neural network; GTSRB; Image Processing; Intelligent transportation systems; Traffic sign.