Deep Pre-Trained Models for Computer Vision Applications: Traffic sign recognition

Soulef Bouaafia, Seifeddine Messaoud, Amna Maraoui, Ahmed Chiheb Ammari, Lazhar Khriji, Mohsen MacHhout

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

Objects detection and Recognition are an important task for computer vision field and intelligent transportation systems. Generally, these tasks remain challenging for the artificial machines due to the need of pre-learning phase in which the machine acquires an intelligent brain. Some researchers have shown that deep learning tools work well in computer vision, image processing, and pattern recognition. To solve such tasks, this paper focuses on deep Convolutional Neural Network (CNN) and its architectures, such as, VGG16, VGG19, AlexNet, and Resnet50. An overview for the techniques and schemes used for computer vision applications such as Road Sign Recognition will be introduced. Then by customizing the hyperparameters for each pre-Trained models, we re-implement these models for the traffic sign recognition application. In the experiments, these pre-Trained CNN classifiers are trained and tested with the German Traffic Sign Recognition Benchmark dataset (GTSRB). Experimental results show that the proposed scheme achieved a good performance results in terms of evaluations metrics of traffic signs recognition. A performance comparison analysis between the selected pre-Trained models for traffic sign recognition confirmed that the AlexNet model outperforms all other implemented models.

Original languageEnglish
Title of host publication18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages23-28
Number of pages6
ISBN (Electronic)9781665414937
DOIs
Publication statusPublished - Mar 22 2021
Event18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021 - Monastir, Tunisia
Duration: Mar 22 2021Mar 25 2021

Publication series

Name18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021

Conference

Conference18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
Country/TerritoryTunisia
CityMonastir
Period3/22/213/25/21

Keywords

  • Deep Convolutional Neural Network
  • Deep learning
  • Pre-Trained models
  • Traffic sign recognition

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering

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