Reducing Data Costs- Transfer Learning Based Traffic Sign Classification Approach

Sarku, Enoch; Steele, Jonathan; Ruffin, Tyesha; Gokaraju, Balakrishna; Karimodini, Ali

doi:10.1109/southeastcon45413.2021.9401900

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…Sarku et al, (2021) [ 33 ] introduced a novel approach to traffic sign recognition by leveraging several Residual Neural Networks (ResNets), including ResNet18, ResNet50, and ResNet152. These ResNets had 18, 50, and 152 weighted hidden layers, respectively.…”

Section: Deep Learning For Traffic Sign Recognitionmentioning

confidence: 99%

Recent Advances in Traffic Sign Recognition: Approaches and Datasets

Lim

Lee

Lim

et al. 2023

Sensors

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Autonomous vehicles have become a topic of interest in recent times due to the rapid advancement of automobile and computer vision technology. The ability of autonomous vehicles to drive safely and efficiently relies heavily on their ability to accurately recognize traffic signs. This makes traffic sign recognition a critical component of autonomous driving systems. To address this challenge, researchers have been exploring various approaches to traffic sign recognition, including machine learning and deep learning. Despite these efforts, the variability of traffic signs across different geographical regions, complex background scenes, and changes in illumination still poses significant challenges to the development of reliable traffic sign recognition systems. This paper provides a comprehensive overview of the latest advancements in the field of traffic sign recognition, covering various key areas, including preprocessing techniques, feature extraction methods, classification techniques, datasets, and performance evaluation. The paper also delves into the commonly used traffic sign recognition datasets and their associated challenges. Additionally, this paper sheds light on the limitations and future research prospects of traffic sign recognition.

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Section: Deep Learning For Traffic Sign Recognitionmentioning

confidence: 99%