## 2 Related Works
### TSR Process
#### 2.1.3 Traffic Sign Detection (TSD) Module  
The traffic sign detection step aims to find locations and sizes of traffic signs in natural image scenes. Actually, the efficiency and the processing time of the detection process are important factors. They play a significant role in the entire system by reducing the search space and indicating only the road sign in the image. Most traffic sign detection methods are based either on the sign's colour and/or shape information or on machine learning and deep learning methods [19]. Colour-based traffic sign detection methods aim to find the interesting area from the captured frame based on the traffic signs colours. They are largely impacted by illumination, bad weather conditions, different colours, time of the day, and reflection of the sign's surface [20]. Referring to [21; 22], HSV has the most robust segmentation performance compared to other colour spaces thanks to its invariance to illumination changes and its robustness to conditions like occlusion. Shape-based traffic sign detection methods consist of finding contours and approximating them to reach a final decision based on the number of contours. Experimental results show that these methods are generally more robust than colorimetric methods because the variation in daylight or colour does not affect them. However, shape-based detection methods are sensitive to small and vague traffic signs and are costly in computation time [19]. The EDC Circles method proposed by [8] is applied to the GTSDB dataset and has achieved results of 93.78% for Prohibitive and 75.51% for Mandatory detection signs in 36 ms/image. Colour- and shape-based traffic sign detection methods are based on both colour and shape characteristics. They often rely on good colour enhancement and suitable parameters [23; 24]. They share a common deficiency in several factors, like lighting changes, occlusion, translation, rotation, and scale change. The authors in [2] used the Improved HSV and HOG technique on GTSDB and achieved detection rates of 98.67%, 96.67%, and 90.43% for Prohibitive, Danger, and Mandatory signs, respectively. Machine Learning and Deep Learning-based traffic sign detection methods can detect traffic signs accurately and treat the deficiencies shared by the previous methods, like lighting changes, occlusion, translation, rotation, and scale changes. Most of the machine learning-based detection methods shown in Table 1 use handcrafted features to extract the traffic sign, like AdaBoost detection methods and SVM methods, whereas the deep learning-based detection methods shown in Table 2 learn features through Convolutional Neural Networks (CNNs). The Haar-like cascade features method was developed by [25] in order to detect faces in real time and was then used to detect other objects like traffic signs by [26; 27]. The Support Vector Machine (SVM) uses HOG features to express the objects and to treat the detection problem of the object as an SVM classification problem in which each candidate is classified into objects or backgrounds [28]. According to [4], the Haar-like features method is faster and more useful than the HOG-like features method in detecting blurry and faded signs in various lighting conditions. Moreover, AdaBoost-based detection methods do not need a Region of interest (ROI) extraction process, whereas SVM-based methods often need the extraction process to get these regions, which greatly affects the performance of the SVM-based TSD detectors. Most CNN-based detection networks are generally slow to detect signs. However, some networks, like You Only Look Once, have fast performance (YOLO) [6; 29]. The application of Faster RCNN to the German Traffic Sign Detection Benchmark dataset (GTSDB) achieved 84.5% accuracy in 261 ms, compared to 94.2% accuracy in 155 ms for the Haar-like cascade technique [25; 30].