Year: 2022
Type: article
Abstract: Recently, in-depth learning about computer vision and object classification tasks has surpassed other machine learning (ML) algorithms. This algorithm, alike similar ML algorithms, requires a dataset for training. In most real cases, developing an appropriate dataset is expensive and time-consuming. Also, in some situations, providing the dataset is unsafe or even impossible. In this paper, we proposed a novel framework for traffic sign recognition using synthetic data and deep learning. The main feature of the proposed method is its independence from the real-life dataset, which leads to high accuracy in the real test dataset. Creating one-by-one synthetic data is more labor-intensive and costlier than providing real data. To tackle the issue, the proposed framework uses a procedural method, which gives the possibility to develop countless high-quality data that are close enough to the real data. Due to its procedural nature, this framework can be easily edited and tuned.
DOI: 10.54646/bijcicn.002
Authors: Avaz Naghipour, Rahim Pasbani
Institution: University College of Nabi Akram
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4313138239