Enhanced Traffic Sign Recognition with Ensemble Learning

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

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.

Original languageEnglish
Article number33
JournalJournal of Sensor and Actuator Networks
Volume12
Issue number2
DOIs
Publication statusPublished - Apr 2023
Externally publishedYes

Keywords

  • convolutional neural network
  • ensemble learning
  • traffic sign recognition

Fingerprint

Dive into the research topics of 'Enhanced Traffic Sign Recognition with Ensemble Learning'. Together they form a unique fingerprint.

Cite this