Traffic Sign Classification and Detection using Efficient Machine Learning Technique

Abstract— Traffic sign classification and detectionplay a crucial role in ensuring road safety by assistingautonomous vehicles, traffic monitoring systems, anddriver assistance technologies. Traffic signs providecritical information, such as speed limits, roadconditions, and warnings, making their accuratedetection and classification essential for reducing roadaccidents and improving traffic efficiency. This paperexplores an efficient Support Vector Machine (SVM)machine learning technique for the classification anddetection of traffic signs. The proposed approachleverages SVM's ability to handle high-dimensional dataand its robustness to overfitting for effectiveperformance in real-world traffic scenarios. Keycontributions include preprocessing steps to enhanceimage quality, feature extraction using Histogram ofOriented Gradients (HOG), and hyperparameteroptimization of the SVM model to improve detectionaccuracy and computational efficiency. Extensiveexperiments were conducted on benchmark datasets tovalidate the performance of the proposed technique.