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
Divide and Conquer Approach to Long Genomic Sequence Alignment
Mahmoud Naghibzadeh - Samira Babaei - Behshid Behkmal - Mojtaba Hatami
Real-Time Forecasting Using Mixed Frequency Time-Series Data
Armin Khayati - Mohammad Taheri - Koorush Ziarati
Crack Segmentation in Civil Structure Images Using a Deep Learning Based Multi-Classifier System
Mohammadreza Asadi - Seyedeh Sogand Hashemi - Mohammad Taghi Sadeghi
Zone-Based Federated Learning in Indoor Positioning
Omid Tasbaz - Vahideh Moghtadaiee - Bahar Farahani
Histopathology Image-Based Cancer Classification Utilizing Transfer Learning Approach
Amir Meydani - Alireza Meidani - Ali Ramezani - Maryam Shabani - Mohammad Mehdi Kazeminasab - Shahriar Shahablavasani
UAV-based Firefighting by Multi-agent Reinforcement Learning
Reza Shami Tanha - Mohsen Hooshmand - Mohsen Afsharchi
Classification of Audio Streaming in Network Traffic Based on Machine Learning Methods
Mohammad Nikbakht - Mehdi Teimouri
Capturing Local and Global Features in Medical Images by Using Ensemble CNN-Transformer
Javad Mirzapour Kaleybar - Hooman Saadat - Hooman Khaloo
Chaotic multi-population ABC algorithm based on memory and levy flight for solving dynamic job shop scheduling problems
Mohammad Ali Zarif - Javad Hamidzadeh
Instance Selection from Skewed Class Distributions by Using the multi-objective optimizer
Mona Moradi - Javad Hamidzadeh
more
Samin Hamayesh - Version 42.2.1