0% Complete
Home
/
13th International Conference on Computer and Knowledge Engineering
Traffic Sign Recognition Using Local Vision Transformer
Authors :
Ali Farzipour
1
Omid Nejati Manzari
2
Shahriar B. Shokouhi
3
1- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
2- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
3- School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Keywords :
vision transformer،Deep Learning،Traffic Sign Recognition،self-driving vehicles
Abstract :
Abstract—Recognition of traffic signs is a crucial aspect of self- driving cars and driver assistance systems, and machine vision tasks such as traffic sign recognition have gained significant attention. CNNs have been frequently used in machine vision, but introducing vision transformers has provided an alternative approach to global feature learning. This paper proposes a new novel model that blends the advantages of both convolutional and transformer-based networks for traffic sign recognition. The proposed model includes convolutional blocks for capturing local correlations and transformer-based blocks for learning global dependencies. Additionally, a locality module is incorporated to enhance local perception. The performance of the suggested model is evaluated on the Persian Traffic Sign Dataset and German Traffic Sign Recognition Benchmark and compared with SOTA convolutional and transformer-based models. The experimental evaluations demonstrate that the hybrid network with the locality module outperforms pure transformer-based models and some of the best convolutional networks in accuracy. Specifically, our proposed final model reached 99.66% accuracy in the German traffic sign recognition benchmark and 99.8% in the Persian traffic sign dataset, higher than the best convolutional models. Moreover, it outperforms existing CNNs and ViTs while maintaining fast inference speed. Consequently, the proposed model proves to be significantly faster and more suitable for real-world applications.
Papers List
List of archived papers
Robust Learning to Learn Graph Topologies
Navid Akhavan Attar - Ali Fahim
Automatic Generation of XACML Code using Model-Driven Approach
Athareh Fatemian - Bahman Zamani - Marzieh Masoumi - Mehran Kamranpour - Behrouz Tork Ladani - Shekoufeh Kolahdouz Rahimi
Pyramid Transformer for Traffic Sign Detection
Omid Nejati manzari - Amin Boudesh - Shahriar B. Shokouhi
Ramp Progressive Secret Image Sharing using Ensemble of Simple Methods
Atieh Mokhtari - Mohammad Taheri
Automated Person Identification from Hand Images\\using Hierarchical Vision Transformer Network
Zahra Ebrahimian - Seyed Ali Mirsharji - Ramin Toosi - Mohammad Ali Akhaee
An Analysis of Botnet Detection Using Graph Neural Network
Faezeh Alizadeh - Mohammad Khansari
An Interactive Approach for Query-based Multi-Document Scientific Text Summarization
Mohammadsadra Nejati - Azadeh Mohebi - Abbas Ahmadi
Stock market prediction using multi-objective optimization
Mahshid Zolfaghari - Hamid Fadishei - Mohsen Tajgardan - Reza Khoshkangini
Histopathology Image-Based Cancer Classification Utilizing Transfer Learning Approach
Amir Meydani - Alireza Meidani - Ali Ramezani - Maryam Shabani - Mohammad Mehdi Kazeminasab - Shahriar Shahablavasani
LPCNet: Lane detection by lane points correction network in challenging environments based on deep learning
Sina BaniasadAzad - Seyed Mohammadreza Mousavi mirkolaei
more
Samin Hamayesh - Version 41.7.6