Traffic sign identification on rainy conditions using K means algorithm comparison with KNN and SVM

Abstract

The aim of this study is to detect and identify the Traffic signals on rainy conditions using a K-means algorithm and correlate with the KNN and Innovative SVM. A total of 370 samples were collected from different road signals available in kaggle. These samples were divided into a training dataset of 185 (50%) and a test dataset 185 (50%). Accuracy values were calculated to quantify the performance of the K-means algorithm comparison with K Nearest Neighbour (KNN) and Innovative SVM and G power=0.8. On performing independent samples T-test on the three groups considered. The K-means algorithm has a 87% accuracy rate, KNN has an 88% accuracy rate and Innovative SVM has a 96% accuracy rate. Finally, Innovative SVM appears significantly better than K-mean and KNN. The statistical significance value is 0.001 (p<0.05). From the analysis of the experimental results, the Innovative Support Vector Machine algorithm (SVM) gives better results than the K means and KNN for the detection of the Traffic signals.