Improving Driver Safety through Automated Traffic Sign Recognition Systems

Author(s)	Ms. Saloni Gupta, Ms. Shubhi Gupta, Prof. Dr. Maria Jamal
Country	India
Abstract	This research presents a CNN-based automated traffic sign recognition system designed to enhance driver safety by accurately detecting and classifying road signs in real time. The proposed TS-CNN model is trained on the GTSRB dataset, incorporating techniques like HSV segmentation, shape-based feature extraction, and data augmentation to improve robustness under various conditions. With a classification accuracy of 95% and fast inference speed, the system aids in reducing accidents caused by missed or misunderstood traffic signs, making it suitable for real-world driver assistance applications.
Keywords	Traffic Sign Recognition, Convolutional Neural Network (CNN), Intelligent Transportation Systems, Driver Assistance, Real-Time Classification, Deep Learning, GTSRB Dataset, Image Processing, Road Safety, Feature Extraction
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-04
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.43612

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International Journal For Multidisciplinary Research