iELMNet: An Application for Traffic Sign Recognition using CNN and ELM

Abstract

Traffic Sign Recognition (TSR) is a crucial step for automated vehicles and driver assistance systems. Automated TSD in an extreme environment has always been challenging due to foggy, rainy, blurry, and cropping images. A real-time TSD model named improved Extreme Learning Machine Network (iELMNet) is proposed to tackle these challenges. Primary modules of iELMNet include: a) Custom DensNet; b) Accurate Anchor Prediction Model (A2PM); c) Scale Transformation (ST), and d) Extreme Learning Machine (ELM) classifier. Convolutional Neural Network (CNN) model improvises edges of traffic-signs using mapped images extracted from handcrafted features. A2PM removes the redundant features to improve efficiency. ST is utilized to allow the proposed technique for detecting these signs of variant sizes. ELM classifier tries to classify traffic signs robustly by minimizing the feature dimensions. The proposed model is evaluated over three publicly available datasets, i.e., CURE-TSR, TT100k, and GTSRB, and acquired 98.63%, 95.22%, and 99.45% precision, respectively. The output of proposed model demonstrates its competence and ability to implement it in a practical environment.