Real-time traffic light recognition is essential for autonomous driving. Yet, a cohesive overview of the underlying model architectures for this task is currently missing. In this work, we conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs). We focus on two essential aspects: datasets and CNN architectures. Based on an underlying architecture, we cluster methods into three major groups: (1) modifications of generic object detectors which compensate for specific task characteristics, (2) multi-stage approaches involving both rule-based and CNN components, and (3) task-specific single-stage methods. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.


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    Title :

    Traffic Light Recognition using Convolutional Neural Networks: A Survey


    Contributors:


    Publication date :

    2023-09-24


    Size :

    1773591 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




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