Traffic sign classification via Semi-Supervised model with uncertain labels
Luhui Yang, Qing Liu, Yun Yang, Po Yang · 2020
Traffic sign classification is the core of intelligent transportation and fundamental for constructing an automatic driving system. While supervised classification tasks demonstrate promising classification performance, a particular challenge is how to ensure the confidence for collecting labelled data. This paper presents a semi-supervised approach via label confidence for traffic sign classification to avoid the interference of uncertain labelled data. The idea of the proposed approach is to compare unsupervised information of the data, the supervised information carried by the learning data, and the supervised information which is given by the classification model in order to detect inconsistencies. The approach is able to build a robust classification model. Experimental results on benchmark and real-world dataset demonstrate that our approach significantly outperforms the existing approaches when uncertain labelled data exists.