Detection of traffic signs under various conditions using Random Forest algorithm comparison with KNN and SVM

The aim of this study is to detect and identify the Traffic signals on rainy conditions using a Random Forest 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 KNN and Innovative SVM. The study parameters are alpha =0.03, Gpowei-0.8. On performing an independent samples T-test on the three groups considered. The Random Forest algorithm has a 86% accuracy rate, KNN has an 88% accuracy rate and Innovative SVM has a 96% accuracy rate. Finally, Innovative SVM appears significantly better than Random Forest 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 Random Forest and KNN for the detection of the Traffic signals.