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Objective <p>In intelligent transportation systems, accurate and efficient traffic sign recognition is essential for enhancing road safety and traffic management. This study proposes DF-CNN, a lightweight Deep Fusion Convolutional Neural Network designed to achieve high classification accuracy while minimizing computational complexity and energy consumption in alignment with Green AI principles.</p> Methodology <p>The proposed model is trained and evaluated on the GTSRB dataset comprising 43 traffic sign classes. The architecture follows a sequential convolutional design with hierarchical feature integration, enabling efficient feature extraction with reduced model complexity. The framework incorporates preprocessing, optimized training, and fivefold cross-validation to ensure robustness and reliability.</p> Results <p>Experimental results demonstrate that the proposed DF-CNN achieves a classification accuracy of 98.49% ± 0.21. The model maintains stable convergence behavior and achieves this performance with approximately 162&#xa0;K trainable parameters and ~ 29&#xa0;M MACs, indicating strong computational efficiency.</p> Conclusion <p>The proposed DF-CNN provides a balance between high accuracy and low computational cost, making it suitable for real-time traffic sign recognition and resource-constrained environments. The model aligns with Green AI principles by reducing energy consumption while maintaining strong predictive performance.</p>

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Deep fusion convolutional neural network for accurate and carbon efficient traffic sign recognition

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Manish Rai,
  • Abhishek Kesarwani,
  • Mohit Gangwar

摘要

Objective

In intelligent transportation systems, accurate and efficient traffic sign recognition is essential for enhancing road safety and traffic management. This study proposes DF-CNN, a lightweight Deep Fusion Convolutional Neural Network designed to achieve high classification accuracy while minimizing computational complexity and energy consumption in alignment with Green AI principles.

Methodology

The proposed model is trained and evaluated on the GTSRB dataset comprising 43 traffic sign classes. The architecture follows a sequential convolutional design with hierarchical feature integration, enabling efficient feature extraction with reduced model complexity. The framework incorporates preprocessing, optimized training, and fivefold cross-validation to ensure robustness and reliability.

Results

Experimental results demonstrate that the proposed DF-CNN achieves a classification accuracy of 98.49% ± 0.21. The model maintains stable convergence behavior and achieves this performance with approximately 162 K trainable parameters and ~ 29 M MACs, indicating strong computational efficiency.

Conclusion

The proposed DF-CNN provides a balance between high accuracy and low computational cost, making it suitable for real-time traffic sign recognition and resource-constrained environments. The model aligns with Green AI principles by reducing energy consumption while maintaining strong predictive performance.