Road and Sight Functions Detection and Recognition Using Stacked Sparse AutoEncoders (SSAE)

Abstract

Road accidents are one of the main sources of horribleness and death rate accounting one million passings for every year. In unfriendly rush hour conditions, the driver may not notify traffic signs which may cause mishaps. The main objective of the paper is to detect and recognize the traffic sign using deep learning model using Stacked Sparse AutoEncoders (SSAE) which uses the enriched dataset of German traffic signs to sustainably increase the accuracy and to give less computational time using SSAE than the previous approaches. The advancement of the framework incorporates following working stages, image preprocessing, detection and recognition. The proposed method uses RGB color segmentation and shape matching can be followed by Stacked Sparse AutoEncoders which gives the result of accuracy (100%) and processing time (5.5ms) per frame. SSAE introduces the sparse representation based on optimized kernel function to improve the image classification effect. It has an advanced level features from pixel intensities and the  advanced level features produced with the help of autoencoders to give the better classification of traffic sign images. The created algorithm is executed using NodeMCU hardware platform.