## 3 Proposed TSR Methodology  
TSR systems are performant only if they achieve a high accuracy rate with a short processing time. In fact, the TSR system must detect and classify a road sign on the roadside in order of appearance, especially in the case of high-speed driving. Thus, our methodology is at the heart of this context. It combines the two best methods of detection (Haar cascade detector) and classification (Deep learning CNN model with attention mechanism) in terms of accuracy and detection time.  
The recognition process, as shown in Figure 3, begins with capturing frames using a real-time camera. In order to reduce the impact of object detection problems like high speeds, the weather, the time of day, and many external noises such as fading and blurring effects, affected visibility, multiple appearances of the sign, chaotic backgrounds, viewing angle problems, damaged and partially obscured signs, etc., numerous configurations must be applied to the camera before or at the time of the image acquisition, like adjusting the High Dynamic Range (HDR), image resolution, image size, etc.  
Preprocessing techniques are then applied to the input frame to improve its quality and consistency and make it easier for the detection algorithm to identify the signs. In our case, a colour space conversion technique is used to convert the RGB image to greyscale and resize the greyscale image to 32 \(\times\) 32 pixels.  
In the detection step, the Haar cascade technique begins by extracting Haar-like features from input images and then builds cascaded classifiers embedded into the AdaBoost classifier to discard negative images. After the training process, the obtained Haar cascade detector is applied to the preprocessed acquired image to extract the traffic sign image, which is then processed with the trained CNN model developed with the attention mechanism in order to be identified.  
In fact, in the classification step, we choose to use the GTSRB dataset to train the developed deep learning model for many reasons, including: it contains forty-three classes, with approximately more than 50,000 pixelated low-resolution and contrast images, describing four categories of road signs (Warning, Regulatory, Obligatory, and Priority), and considering different driving conditions (blurring, lighting, etc.).  
In order to improve the contrast of the training images, CLAHE (Contrast Limited Adaptive Histogram Equalisation), a preprocessing filter is performed on traffic sign images, which are already resized to 32 \(\times\) 32 pixels. At the end, our approach is tested first on the CPU and then on a development board in an embedded system to validate the efficiency of our proposed TSR system.  
The steps of our methodology are summarised in the algorithm below:  
Step 1: prepare Haar-like cascade detectors;  
Figure 3: Proposed Traffic Sign Recognition methodology.  
Step 2: develop a deep neural network based on CNNs using attention mechanisms;  
Step 3: train and test the CNN model on the GTSRB dataset;  
Step 4: save the trained model;  
Step 5: capture and preprocess the image from the video camera;  
Step 6: extract the ROI from the captured image using the Haar-like cascade-trained detector;  
Step 7: prediction of the detected road sign using the trained model;  
Step 8: validation on the CPU and on a development board in an embedded system.