Traffic Sign Recognition and Classification Using YOLOv2, Faster RCNN and SSD

Abstract

In the walk of Advanced Driving Assistance Systems (ADAS), Intelligent Driving and Traffic safety, Object detection plays a crucial role in the upcoming genesis of self-governing vehicles. Traditional computer vision and machine learning advances for object detection confront challenges against the difficult image backgrounds and environment conditions like sunlight effects, barricades and occlusions. In this paper, Single Shot Detector (SSD), Faster Region Convolutional Neural Network (Faster RCNN) and You Only Look Once (YOLOv2) deep learning architectures are compared by applying distinct pretrained Convolutional Neural Network (CNN) models. Experiments have been organized in a wide range to attain distinct models of Faster RCNN, SSD and YOLOv2 through appropriate modification in algorithms and parameters tuning. In this work, SSD, Faster RCNN and YOLOv2 are trained for 5 different object classes of traffic signs and their outcomes are evaluated. Traditional Evaluation parameters: mAp(mean Average precision-Precision, Recall and IoU) and FPS(Frames per second) are run-down to analyze the accuracy and speed of the algorithms. On analyzing, the accuracy of YOLOv2 outperforms Faster RCNN and SSD by 3.5% and 21% respectively. Also, YOLOv2 learned 3 times speedy than Faster RCNN with increased accuracy.