## 3 Proposed TSR Methodology
### Traffic Sign Classification (TSC) Step
#### 3.2.1 GTSRB Dataset  
The GTSRB dataset was collected by the Institute of Neural Information Processing (INI) at the University of Ulm in Germany [44].  
The techno-economic parameters of this dataset can refer to various aspects related to it, such as:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**TSC Methods** & **CR (\%)** & **Dataset** & **Classification Time (s)** \\ \hline ML method: HOG + CLBP + & \multirow{2}{*}{99.10} & GTSRB & \multirow{2}{*}{3.68} \\ Gabor ELM [33] & & & (48 \(\times\) 48 images) & \\ \hline DL methods: three CNN [14] & 99.70 & GTSRB & 24.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Best obtained classification results with the GTSRB dataset.  
Figure 5: Detection and extraction of ROIs using a Haar-like cascade detector.  
Figure 6: Traffic Sign Classification process.  
* Size: this dataset involves 43 road sign classes as described in Figure 7[44], with 51,839 images (34,799, 4410, and 12,640 for training, validation, and testing, respectively) collected from real-world traffic scenes in Germany;
* Image resolution: the images in the GTSRB dataset are of various resolutions, ranging from 15 \(\times\) 15 pixels to 250 \(\times\) 250 pixels;
* Annotation: The dataset was annotated by human experts who manually labelled each image with the corresponding traffic sign class. The annotations include both the bounding box coordinates for the traffic sign and the corresponding class label and were performed using an online annotation tool specifically designed for the task of traffic sign recognition;
* Diversity: It contains a diverse set of traffic signs, including speed limit signs, warning signs, and prohibition signs. Some traffic signs may be overrepresented or underrepresented in the dataset due to differences in their frequency in real-world traffic scenes. In fact, the distribution of the number of samples used to train the model per class shown in Figure 8 is not uniform. The largest classes have 10 times more traffic sign images than the smallest ones. This is normal since, in real life, some signs appear more frequently than others. To address this, the dataset includes a training-validation-test split that helps ensure that the models trained on the dataset are evaluated on a representative set of data;
* Collection method: the GTSRB dataset was collected using a camera mounted on a vehicle and taken under different conditions (blur, lighting, etc.) to ensure a fair evaluation of model performance;
* Availability: this dataset is freely available for academic and research purposes;
* Usage: It is commonly used for evaluating the performance of various traffic sign recognition algorithms, including deep learning-based approaches. The dataset has been used in various competitions, challenges, and research projects related to traffic sign recognition [44]. The use of this dataset has led to the development of many state-of-the-art traffic sign recognition algorithms, which have potential applications in areas such as autonomous driving and road safety;
* Complexity: the GTSRB dataset is considered challenging due to the large number of classes and the variation in the appearance of traffic signs due to lighting conditions, weather, and occlusion.  
Figure 7: Traffic Sign Classes.