Applying balancing techniques in traffic sign recognition

Abstract

Traffic Sign Recognition systems aim to determine the meaning of traffic signs in highways for real-world applications such astraffic sign inventory or driver assistance systems. Traffic sign datasets are inherently imbalanced, i.e. some traffic signs appearmore frequently than others. One serious consequence of this imbalance is the low recognition rates of minority classes (classeswith fewer training cases). In this paper, we propose a new method for improving traffic sign recognition of minority classes, byapplying balancing algorithms. As a result, our proposed method improves minority class recognition rates up to 28% comparedto traditional methods.