Citation

Lim, Xin Roy and Lee, Chin Poo and Lim, Kian Ming and Ong, Thian Song (2023) Enhanced Traffic Sign Recognition with Ensemble Learning. Journal of Sensor and Actuator Networks, 12 (2). p. 33. ISSN 2224-2708

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Abstract

With the growing trend in autonomous vehicles, accurate recognition of traffic signs has become crucial. This research focuses on the use of convolutional neural networks for traffic sign classification, specifically utilizing pre-trained models of ResNet50, DenseNet121, and VGG16. To enhance the accuracy and robustness of the model, the authors implement an ensemble learning technique with majority voting, to combine the predictions of multiple CNNs. The proposed approach was evaluated on three different traffic sign datasets: the German Traffic Sign Recognition Benchmark (GTSRB), the Belgium Traffic Sign Dataset (BTSD), and the Chinese Traffic Sign Database (TSRD). The results demonstrate the efficacy of the ensemble approach, with recognition rates of 98.84% on the GTSRB dataset, 98.33% on the BTSD dataset, and 94.55% on the TSRD dataset.

Item Type:	Article
Uncontrolled Keywords:	traffic sign recognition; convolutional neural network; ensemble learning
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Jun 2023 00:55
Last Modified:	02 Jun 2023 00:55
URII:	http://shdl.mmu.edu.my/id/eprint/11441

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