Deep Residual Network with Pelican Cuckoo Search for Traffic Sign Detection

Abstract

The timely and precise discovery of traffic signs is considered an effective part of modeling automated vehicle driving. However, the dimension of traffic signs accounted for a lower ratio of input pictures which elevated the complexity of discovery. Hence, a new model is devised using faster region-based convolution neural network (faster R-CNN) traffic for detecting traffic signs. The Region of Interest (RoI) extraction and Median filter are executed for discarding the superfluous data from the dataset. The method extracted a Pyramid Histogram of Oriented Gradient (PHoG), local vector pattern (LVP), CNN and ResNet features for generating beneficial information. It is used to lessen the loss of contextual data and the data augmentation is further applied for making the training of the model more stable and time-saving. The traffic sign recognition is executed with faster R-CNN wherein the tuning of hyperparameters such as batch normalization rate, epoch and learning rate is determined by the proposed pelican cuckoo search (PCS). The method revealed improved efficacy without presenting additional computational costs in the model. Moreover, the faster R-CNN is termed the finest technique to enhance the discovery of traffic signs. The proposed PCS-based faster R-CNN outperformed with the highest precision 92.7%, specificity of 93.7% and [Formula: see text]-measure of 93.2%.