## 2 Related Work  
Traffic sign recognition has been a hot research field for the last decade. It is an essential module for autonomous cars and an automatic driver assistance system (ADAS). Various approaches have been pitched for accurate and real-time traffic sign detection. Since traffic signs follow standard color and shape, prime approaches have been based on color and shape detection. Researchers exploited the use of these features to detect and recognize traffic signs. [PERSON] and [PERSON] [2] proposed a traffic sign detection and recognition framework based on Grassmann Manifolds. It uses HSV color segmentation for detection. The authors in [11] enhanced red and blue channels of an RGB image. Histogram of Oriented Gradient (HOG) was applied and an SVM and KNN classifier was used to classify the traffic sign and non-traffic sign regions. [PERSON] et al. in [12] proposed a Colour Probability Model to determine the color distribution of traffic signs. In this work the authors used Ohta space [13] instead of RGB space. The authors in [14] incorporated the constrained Hough Transform to determine the shape of traffic signs. Other methods such as the Radial symmetry detector [15] used traffic sign appearance features. It should be noted that the color and shape-based detectors are not promising for traffic sign detection in real-time road scenarios, since these features are affected by illumination, weather conditions, and occlusions. Moreover, background objects with similar shapes and colors may trigger false detections. Some research works addressed the aforementioned issues using its standard shape and color as its unique identity. The authors in [16] used different channels of shape and color features for traffic sign detection. The researchers in [17] managed traffic sign detection by extracting a region of interest (ROIs) with the help of Maximally Stable Extremal Regions (MSERs) [18]. The recent developments in deep learning proved that CNN is more effective towards traffic sign detection than handcrafted features. CNN-based detectors consist of two categories: two-stage and single-stage detectors. The former detection technique is more robust in detecting traffic signs with higher accuracy than the later one. [PERSON] et al. [19] proposed a three-stage traffic sign recognition system. This system was composed of three main components i.e., Faster RCNN [3] detector, Hough transform for localization refinement, and CNN-based classifier. The authors in [1] [PERSON] et al. used Mask RCNN [6] for traffic sign detection and localization. They also proposed CNN based classifier to predict more than two traffic signs. The system was able to achieve 96.16% mAP in the GTSDB dataset [3] with the processing speed of 3.3 FPS. Although the performance of the algorithm in [1] is outstanding, 30 FPS is required for real-time detection. Single-stage detectors can achieve real-time traffic sign detection. Single-stage detectors determine bounding box coordinates and classification scores simultaneously in a single run hence, improving inference speed for detectors. Single-stage detectors by their architectures were designed to avoid region proposal or sliding windows. [PERSON] and [PERSON] [20] proposed a traffic sign detection system that used Single Shot Detector [7] to output precise boundary corners of the detected traffic sign. The authors in [21] used the SSD model with Feature Pyramid Network [22] for multi-scale traffic sign detection. To overcome the low inter-class variation in traffic signs, a lower-level feature map was used for classification. The authors of [23] used Gaussian modeling to determine the uncertainty in bounding box coordinates predicted by YOLOv3, which was further used to enhance the detection accuracy. A modified YOLOv3 based traffic sign recognition system was proposed in [24], obtaining an average precision of 52.32% for LSITSD [25] test images with higher inference time than state-of-the-art systems. Overall, most of the proposed detectors are either accurate or fast.