Lightweight Deep Learning Model for Traffic Light Detection

Bali, Shweta; Kumar, T. Vinoth; Tyagi, S. S.

doi:10.1109/ictacs56270.2022.9988178

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A Modifications Of Generic Object Detectors1

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“…This model, however, only performed detection, not the classification of the TL states. Bali et al [58] tried to replace the YOLOv2 backbone with different lightweight CNNs, whereas the best results were achieved with SqueezeNet [69].…”

Section: A Modifications Of Generic Object Detectorsmentioning

confidence: 99%

German Traffic Sign Recognition Using Convolutional Neural Network

Santosh¹,

Kumar²,

Sandeep³

et al. 2022

2022 6th International Conference on Electronics, Communication and Aerospace Technology

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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 rulebased 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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Section: A Modifications Of Generic Object Detectorsmentioning

confidence: 99%