Transfer Learning Approach for Recognition of Iranian Traffic Signs in Self-Driving Cars

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gorji mohammad; Nasri Farhad; Tabatabaei Azadeh

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2022 | Issue:8

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Title

Transfer Learning Approach for Recognition of Iranian Traffic Signs in Self-Driving Cars

Author(s)

gorji mohammad | Nasri Farhad | Tabatabaei Azadeh

Keywords

Traffic Sign Recognition

Convolutional Neural Networks

Transfer Learning

Machine Learning

Deep Learning

Abstract

In recent years, with the increasing advancement of artificial intelligence and Machine Learning, huge changes have occurred in the field of vehicles and self-driving cars. Recognition and interpretation of urban traffic signs, by machine vision systems, improve the safety of driving operations as one of the basic principles of self-driving vehicles. Due to the high interaction of self-driving vehicles with traffic signs during the movement, creating a system with high accuracy for interpretation and an immediate decision is a big challenge. In this research, with the use of Convolutional Neural Networks, a system is designed that can recognize Iranian traffic signs. By applying the Transfer Learning approach, we train our model with a new collection of traffic signs images that reaches a high accuracy in optimal conditions.

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APA: Copy

gorji, mohammad, Nasri, Farhad, & Tabatabaei, Azadeh. (2022). Transfer Learning Approach for Recognition of Iranian Traffic Signs in Self-Driving Cars. INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/949658/en

Vancouver: Copy

gorji mohammad, Nasri Farhad, Tabatabaei Azadeh. Transfer Learning Approach for Recognition of Iranian Traffic Signs in Self-Driving Cars. 2022. Available from: https://sid.ir/paper/949658/en

IEEE: Copy

mohammad gorji, Farhad Nasri, and Azadeh Tabatabaei, “Transfer Learning Approach for Recognition of Iranian Traffic Signs in Self-Driving Cars,” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2022, [Online]. Available: https://sid.ir/paper/949658/en

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