Comparative Study of Traffic Light and Sign Detection Techniques

Abstract

AbstractIn this paper, we look at the various approaches to traffic light and street sign detection. In the traditional image processing-based approach, we primarily use a circular Hough transform with color masks to detect traffic lights. For the more complex approach, we deploy convolution neural networks in combination with previously proposed feature extractors to aid with traffic light detection along with a confidence score. We make use of the SSD ResNet V1 trained on the COCO dataset in addition to the Inception V3 architecture trained on the ImageNet dataset. All of these experiments are performed on the Berkeley Deep Drive dataset. We compare these techniques based on parameters like accuracy, computation time, and memory usage, and highlight the advantages and disadvantages of each type.KeywordsComputer visionConvolutional neural networksHough transformImage processingStreet sign detectionTraffic light recognition