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.
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