D.PRATHAP REDDY, D.UDAY KIRAN, CH.SRI SAI GNANESH, M.SURESH
Abstract
Traffic sign recognition plays a crucial role in improving road safety and supporting intelligent transportation systems. With the rapid growth of vehicles and increasing traffic complexity, manual recognition of traffic signs by drivers may lead to errors due to factors such as distraction, poor lighting conditions, weather changes, and high driving speed. To overcome these challenges, automated traffic sign recognition systems using Machine Learning techniques have gained significant importance.
This project focuses on Traffic Sign Recognition using a Convolutional Neural Network (CNN) algorithm and a Conventional Neural Network (ANN). The proposed system processes traffic sign images, applies image preprocessing techniques such as resizing and normalization, and classifies the signs into different categories. ANN is used as a baseline model for basic classification, while CNN is employed for efficient feature extraction and accurate recognition of traffic signs from images.
The performance of both ANN and CNN models is analyzed and compared based on accuracy and efficiency. Experimental results show that CNN outperforms the conventional neural network in recognizing traffic signs due to its ability to capture spatial and visual features effectively. This system can be integrated into driver assistance systems and autonomous vehicles to enhance road safety, reduce human error, and support smart transportation solutions.
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How to Cite This Paper
D.PRATHAP REDDY, D.UDAY KIRAN, CH.SRI SAI GNANESH, M.SURESH (2026). TRAFFIC-SIGN-RECOGNIZATION-USING-CNN-ALGORITHM-CONVENTIONAL-NEURO-NETWORK. International Journal of Computer Techniques, 13(3). ISSN: 2394-2231.
