Traffic sign detection and recognition in Jordan based on machine learning and deep learning

Traffic signs provide essential information to drivers, pedestrians, and cyclists on roads, highways, and other public areas, contributing significantly to road safety and order. This research investigates the effectiveness of a novel system for detecting and recognizing traffic signs in Jordan using machine learning and deep learning techniques. Specifically, we propose a methodology that integrates ResNet-50 for feature extraction with Support Vector Machine (SVM) for classification. This system leverages ResNet-50′s ability to extract intricate image features and SVM’s precision in classification tasks, achieving an impressive 83.05 % F1 score in recognizing various Jordanian traffic signs. The proposed approach provides a high-performing solution tailored to Jordanian road conditions, demonstrating that this combination of deep and machine learning techniques is effective for traffic sign recognition in real-world scenarios.