TY - GEN
T1 - Deep Pre-Trained Models for Computer Vision Applications
T2 - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
AU - Bouaafia, Soulef
AU - Messaoud, Seifeddine
AU - Maraoui, Amna
AU - Ammari, Ahmed Chiheb
AU - Khriji, Lazhar
AU - MacHhout, Mohsen
N1 - Funding Information:
This project was funded partially by Sultan Qaboos University (SQU), Deanship of Scientific Research (DSR), under grant number “IG/ENG/ECED/19/01” and partially by OMANTEL under grant number “EG/SQU-OT/18/01”. The authors, therefore, acknowledge SQU and OMANTEL for their financial support.
Publisher Copyright:
© 2021 IEEE.
PY - 2021/3/22
Y1 - 2021/3/22
N2 - 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.
AB - 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.
KW - Deep Convolutional Neural Network
KW - Deep learning
KW - Pre-Trained models
KW - Traffic sign recognition
UR - http://www.scopus.com/inward/record.url?scp=85107502709&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107502709&partnerID=8YFLogxK
U2 - 10.1109/SSD52085.2021.9429420
DO - 10.1109/SSD52085.2021.9429420
M3 - Conference contribution
AN - SCOPUS:85107502709
T3 - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
SP - 23
EP - 28
BT - 18th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 March 2021 through 25 March 2021
ER -