Traffic Sign Recognition Dataset and Data Augmentation
This work addresses data scarcity for traffic sign recognition systems, which is crucial for real-world applications like autonomous driving, but it appears incremental as it builds on existing datasets and methods.
The authors tackled the problem of insufficient training instances for traffic sign recognition, especially for rare sign classes, by proposing a unique data augmentation method based on traffic sign standards, which they verified on the TT100K dataset and found to be efficacious.
Although there are many datasets for traffic sign classification, there are few datasets collected for traffic sign recognition and few of them obtain enough instances especially for training a model with the deep learning method. The deep learning method is almost the only way to train a model for real-world usage that covers various highly similar classes compared with the traditional way such as through color, shape, etc. Also, for some certain sign classes, their sign meanings were destined to can't get enough instances in the dataset. To solve this problem, we purpose a unique data augmentation method for the traffic sign recognition dataset that takes advantage of the standard of the traffic sign. We called it TSR dataset augmentation. We based on the benchmark Tsinghua-Tencent 100K (TT100K) dataset to verify the unique data augmentation method. we performed the method on four main iteration version datasets based on the TT100K dataset and the experimental results showed our method is efficacious. The iteration version datasets based on TT100K, data augmentation method source code and the training results introduced in this paper are publicly available.