An ensemble learning with deep feature extraction approach for recognition of traffic signs in advanced driving assistance systems

Abstract

The research paper introduces an automatic traffic sign identification system tailored for the distinctive challenges posed by Indian traffic scenarios. This system leverages deep learning for feature extraction and ensemble learning for classification, effectively sorting traffic signs into their fundamental categories. The paper underscores the crucial significance of precise traffic sign recognition in fortifying autonomous driving assistance systems (ADAS) and ensuring the secure flow of vehicles on roads. Through extensive evaluation using Indian traffic sign databases, the proposed system exhibits superior performance when compared to existing technologies, significantly augmenting the overall efficiency of the recognition process. The reported performance analysis of 91.10% underscores the system's effectiveness in addressing the complex requirements of traffic sign recognition, thereby mitigating potential risks.