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Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks

Dhruv Toshniwal, Saurabh Loya, Anuj Khot, Yash Marda

TL;DR

This work tackles robust traffic sign detection and recognition for real-world driving scenarios featuring environmental variability and numerous sign categories. It presents a CNN-based TSRS pipeline with data preprocessing to uniform 30×30 inputs, a four-layer CNN architecture, dropout, and a 43-class softmax output evaluated on the GTSRB dataset. The model achieves a testing accuracy of approximately 95.9% (training ~94.8%), demonstrating strong generalization and potential for real-time deployment, with improvements suggested via localization and data augmentation. The study highlights the practical impact of CNNs for traffic sign recognition in safety-critical applications and outlines avenues for enhancing performance on underrepresented or confusing sign categories.

Abstract

In the rapidly evolving landscape of transportation, the proliferation of automobiles has made road traffic more complex, necessitating advanced vision-assisted technologies for enhanced safety and navigation. These technologies are imperative for providing critical traffic sign information, influencing driver behavior, and supporting vehicle control, especially for drivers with disabilities and in the burgeoning field of autonomous vehicles. Traffic sign detection and recognition have emerged as key areas of research due to their essential roles in ensuring road safety and compliance with traffic regulations. Traditional computer vision methods have faced challenges in achieving optimal accuracy and speed due to real-world variabilities. However, the advent of deep learning and Convolutional Neural Networks (CNNs) has revolutionized this domain, offering solutions that significantly surpass previous capabilities in terms of speed and reliability. This paper presents an innovative approach leveraging CNNs that achieves an accuracy of nearly 96\%, highlighting the potential for even greater precision through advanced localization techniques. Our findings not only contribute to the ongoing advancement of traffic sign recognition technology but also underscore the critical impact of these developments on road safety and the future of autonomous driving.

Optimized Detection and Classification on GTRSB: Advancing Traffic Sign Recognition with Convolutional Neural Networks

TL;DR

This work tackles robust traffic sign detection and recognition for real-world driving scenarios featuring environmental variability and numerous sign categories. It presents a CNN-based TSRS pipeline with data preprocessing to uniform 30×30 inputs, a four-layer CNN architecture, dropout, and a 43-class softmax output evaluated on the GTSRB dataset. The model achieves a testing accuracy of approximately 95.9% (training ~94.8%), demonstrating strong generalization and potential for real-time deployment, with improvements suggested via localization and data augmentation. The study highlights the practical impact of CNNs for traffic sign recognition in safety-critical applications and outlines avenues for enhancing performance on underrepresented or confusing sign categories.

Abstract

In the rapidly evolving landscape of transportation, the proliferation of automobiles has made road traffic more complex, necessitating advanced vision-assisted technologies for enhanced safety and navigation. These technologies are imperative for providing critical traffic sign information, influencing driver behavior, and supporting vehicle control, especially for drivers with disabilities and in the burgeoning field of autonomous vehicles. Traffic sign detection and recognition have emerged as key areas of research due to their essential roles in ensuring road safety and compliance with traffic regulations. Traditional computer vision methods have faced challenges in achieving optimal accuracy and speed due to real-world variabilities. However, the advent of deep learning and Convolutional Neural Networks (CNNs) has revolutionized this domain, offering solutions that significantly surpass previous capabilities in terms of speed and reliability. This paper presents an innovative approach leveraging CNNs that achieves an accuracy of nearly 96\%, highlighting the potential for even greater precision through advanced localization techniques. Our findings not only contribute to the ongoing advancement of traffic sign recognition technology but also underscore the critical impact of these developments on road safety and the future of autonomous driving.
Paper Structure (10 sections, 4 equations, 8 figures, 1 table)

This paper contains 10 sections, 4 equations, 8 figures, 1 table.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Data Pre-processing
  5. Convolutional Neural Network
  6. Modelling the Data
  7. Training and model evaluation
  8. Output
  9. Results and Analysis
  10. Conclusion

Figures (8)

  • Figure 1: Flow diagram representing the system architecture for traffic sign recognition, detailing the steps from raw image acquisition to result generation.
  • Figure 2: Flow diagram of CNN
  • Figure 3: Representation of the model
  • Figure 4: Training and validation loss curves over 50 epochs, illustrating the model's learning progression.
  • Figure 5: Confusion matrix showcasing the model's performance in accurately classifying traffic sign images.
  • ...and 3 more figures