0% Complete
Home
/
11th International Conference on Computer and Knowledge Engineering
A Robust Network for Embedded Traffic Sign Recognation.
Authors :
Omid Nejati Manzari
1
Shahriar Baradaran Shokouhi
2
1- School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13144, Iran
2- School of Electrical Engineering, Iran University of Science and Technology, Tehran 16846-13144, Iran
Keywords :
deep neural network, traffic sign recognition, auto-driving, embedded
Abstract :
Traffic sign recognition systems are a key component in real-world applications such as auto-driving and safety and driver assistance. While deep neural networks in recent years have achieved high accuracy in the classification of these traffic signs, there is always the discussion of the high computations of these networks and their many teachable parameters. A significant challenge is to design a compact deep neural network for the application of traffic sign recognition. This paper proposes a network that uses residual blocks in the network to obtain a top-1 accuracy of 99.51 for the German traffic sign recognition benchmark, while the number of parameters is ∼430,000, which is ∼32x fewer than the state-of-the-art. Experiments have been performed to show the network's resistance to destructive factors and its comprehensiveness in the application of traffic sign recognition. The results of these tests show that it is a comprehensive and robust network for the recognition of traffic signs.
Papers List
List of archived papers
Intensity-Image Reconstruction Using Event Camera Data by Changing in LSTM Update
Arezoo Rahmati Soltangholi - Ahad Harati - Abedin Vahedian
EEMC: Energy Efficient Multi-Clustering Using Grey Wolf Optimizer in WSNs
Maryam Ghorbanvirdi - Sayyed Majid Mazinani
An optimal workflow scheduling method in cloud-fog computing using three-objective Harris-Hawks algorithm
Ahmadreza Montazerolghaem - Maryam Khosravi - Fatemeh Rezaee
LPCNet: Lane detection by lane points correction network in challenging environments based on deep learning
Sina BaniasadAzad - Seyed Mohammadreza Mousavi mirkolaei
Cluster Sampling: A Cluster-Driven Sampling Strategy for Deep Metric Learning
Hamideh Rafiee - Ahmad Ali Abin - Seyed Soroush Majd
A Graph-based Feature Selection using Class-Feature Association Map (CFAM)
Motahare Akhavan - Seyed Mohammad Hossein Hasheminejad
ROCT-Net: A new ensemble deep convolutional model with improved spatial resolution learning for detecting common diseases from retinal OCT images
Mohammad Rahimzadeh - Mahmoud Reza Mohammadi
Fast and Accurate Motif Discovery in Protein Sequences Using Parallel Processing with OpenMP
Rahele Mohammadi - Mahmoud Naghibzadeh - Abdorreza Savadi
Improving Machine Learning Classification of Heart Disease Using the Graph-Based Techniques
Abolfazl Dibaji - Sadegh Sulaimany
An intelligent linguistic error detection approach to automated diagnosis of Dyslexia disorder in Persian speaking children
Fatemeh Asghari - Mahsa Khorasani - Mohsen Kahani - Seyed Amir Amin Yazdi - Mahdi Arkhodi Ghalenoei
more
Samin Hamayesh - Version 42.2.1