A Machine Learning Framework for Traffic and Road Sign Classification

This paper presents a novel traffic sign detection and recognition framework for Indian road signs based on a Convolutional Neural Network (CNN) architecture. The proposed approach processes grayscale images and employs a robust CNN classifier for accurate traffic sign recognition. Grayscale images are used because they reduce the dimensionality of the input data without significantly affecting the discriminative features required for classification. Consequently, the computational complexity and processing time are reduced, enabling faster image analysis while maintaining high recognition performance. This makes the proposed approach well suited for intelligent traffic sign recognition systems.   To improve efficiency, the trained CNN model is stored using a pickle file. Saving the trained model eliminates the need for repeated training, thereby reducing computational overhead and allowing the model to be directly loaded for future prediction tasks. Experimental results demonstrate that the segmentation and recognition processes remain robust under varying illumination conditions, leading to reliable traffic sign detection and classification.   The experimental evaluation shows that the proposed CNN-based framework achieves an overall classification accuracy of approximately 99%, demonstrating its effectiveness in recognizing Indian traffic signs. Comparative analysis with existing state-of-the-art methods further indicates that the proposed CNN model consistently outperforms conventional classifiers in terms of recognition accuracy and robustness. These results confirm the suitability of the proposed approach for enhancing the safety and efficiency of intelligent transportation systems.   Although the proposed system provides highly accurate traffic sign detection and recognition, it is currently limited to a subset of traffic sign categories. Future research will focus on extending the capabilities of the system by addressing the following objectives:   A.     Recognize a comprehensive range of road traffic signs. B.     Detect and classify multiple traffic signs present within a single scene. C.     Improve recognition performance for images captured under low-light and nighttime conditions. D.     Expand and diversify the training dataset to enhance model generalization. E.      Develop a real-time traffic sign detection and recognition system suitable for deployment in Advanced Driver Assistance Systems (ADAS) and autonomous vehicles.

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