## 2 Related Works
### TSR Process
#### 2.1.4 Traffic Sign Classification (TSC) Module  
TSC is the second step in the process of road sign recognition. It involves the traditional computer vision and machine learning-based methods, but these were quickly replaced by deep learning-based classifiers. In fact, these methods use various datasets in order to be trained, tested, and validated. The GTSRB dataset is the most used dataset in the literature thanks to its diversity of classes (43 classes), its relevant content of data, and its large number of images and annotations. Tables 3 and 4 summarise some classification rates (CR) obtained after applying machine learning and deep learning-based classification methods to the GTSRB dataset. Machine learning-based classification methods consist of two major stages: in the first stage, image features are extracted to accentuate the differences among the classes, and then they are classified using machine learning algorithms. Actually, handcrafted features such as Histogram of Oriented Gradients (HOG), locally Binary Patterns (LBP), and Gabor filter, among others, refer to properties derived using various algorithms using the existing information in the image itself. They were commonly used with traditional \begin{table}
\begin{tabular}{c c c} \hline \hline
**Machine Learning Traffic Sign Detectors** & **Features** & **Training Methods** \\ \hline AdaBoost-based methods & Haar-like & Features cascade \\ SVM-based methods & HOG features & Linear SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Machine learning-based detection methods. \begin{table}
\begin{tabular}{c c} \hline \hline
**Deep Learning Traffic Sign Detectors** & **Training Methods** \\ \hline \hline  & SVM + CNN \\  & Convolutional neural network (CNN) \\ \hline CNN-based detection methods & FCN and Deep CNN \\  & Faster RCNN \\  & Cascaded convolutional networks \\  & YOLOv2 and YOLOv3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep learning-based detection methods. machine learning techniques for object recognition and computer vision, like SVM [8], Random Forest (RF), K-d trees [32], and ELM [33]. Machine learning classifiers are not only used separately but also in combination. In fact, the authors in [34] use [PERSON]'s theory of belief functions to merge the obtained results from the training of several classifiers (SVM, KNN, MLP, and Random Forest) on 15 classes of the GTSRB dataset in order to find the most accurate combination of classifiers. They reach an accuracy of 99.85% by combining the four classifiers. The deep learning-based classification methods are based on CNN or ConvNet. They use extracted features from images, which eliminates the need for manual feature extraction. In fact, features are not trained but learned while the network trains on a set of images. This makes the deep learning models more accurate for computer vision tasks. CNNs learn feature detection through tens or hundreds of hidden layers. Each layer increases the complexity of the learned features. There are many studies that demonstrate the excellent capacities of CNNs and deep learning for engineering applications. The authors in [35] provide a comprehensive survey of the most important aspects of deep learning and include those enhancements recently added to the field. The authors in [36] discuss various applications of CNNs, including OCR and image recognition, detecting objects in self-driving cars, social media face recognition, and image analysis in medicine. In addition, other studies like [37] propose a surrogate model based on 2D-CNN with hyperparameter optimisation to predict the torsional strength of RC beams. The results show that the proposed model exhibits high performance in predicting the torsional strength of RC beams. The authors in [38] propose a hybrid framework based on PCA, DSAE models, and data fusion for structural damage diagnosis. An enhanced WOA was developed to optimise the meta-parameters of the DSAE model.