Traffic Light Recognition using Convolutional Neural Networks: A Survey

• URL: http://arxiv.org/abs/2309.02158v1
• Date: Tue, 5 Sep 2023 11:50:38 GMT
• Title: Traffic Light Recognition using Convolutional Neural Networks: A Survey
• Authors: Svetlana Pavlitska, Nico Lambing, Ashok Kumar Bangaru and J. Marius Z\"ollner
• Abstract summary: We conduct a comprehensive survey and analysis of traffic light recognition methods that use convolutional neural networks (CNNs) Based on an underlying architecture, we cluster methods into three major groups. We describe the most important works in each cluster, discuss the usage of the datasets, and identify research gaps.
• Score: 4.451479907610764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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