Enhanced Traffic Sign Recognition with Ensemble Learning

Xin Roy Lim, Chin Poo Lee, Kian Ming Lim, Thian Song Ong

Research output: Journal PublicationArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Article number33
JournalJournal of Sensor and Actuator Networks
Issue number2
Publication statusPublished - Apr 2023
Externally publishedYes


  • convolutional neural network
  • ensemble learning
  • traffic sign recognition

ASJC Scopus subject areas

  • Instrumentation
  • Computer Networks and Communications
  • Control and Optimization


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