A Color And Shape?Aware Tsr Model Enhanced By Morphological Filtering And Fuzzy Logic

Real-time traffic sign recognition (TSR) plays a crucial role in intelligent transportation systems (ITS) and autonomous driving technologies. It enhances road safety, ensures efficient traffic rule enforcement, and supports the seamless operation of both autonomous and driver-assist systems. This paper proposes a hybrid TSR model that integrates mathematical morphology, edge detection, and fuzzy logic to accurately identify and classify traffic signs across diverse environmental conditions. The preprocessing stage applies contrast enhancement and Gaussian filtering to improve the visibility of key features. Next, shape- and color-based segmentation using mathematical morphology extracts regions of interest that are likely to contain traffic signs. These regions are then analyzed using a fuzzy inference system (FIS) that evaluates features such as color intensity, geometric shape ratios, anedge sharpness. The fuzzy system handles the inherent ambiguity in visual patterns, enabling robust decision-making. The entire model is developed in MATLAB R2015a, ensuring both computational efficiency and real-time performance. The integration of classical mathematical techniques with fuzzy reasoning allows the system to maintain high accuracy and reliability across a wide variety of traffic scenes. The proposed approach demonstrates significant potential for practical deployment in ITS applications, including smart vehicles and automated road safety systems.