The recognition of traffic signs, also known as TSR, is an essential component of many advanced driver assistance systems (ADAS) and autonomous driving systems (ADSs). Traffic sign detection (TSD), the first critical stage of TSR, is a tough challenge because of the various types of traffic signs, their small proportions, difficult driving conditions, and obstructions. TSD methods based on machine recognition and pattern classification have increased recently. This study's primary goal is to research Traffic Sign Detection using Deep Learning Techniques, and the method used in this research is Deep Learning Models like Faster R-CNN and YOLOv5. Convolutional Neural Networks are used for identifying and classifying road signs because they begin with an input image, assign weights to various elements of that image, and then differentiate those weights' respective aspects from one another. According to the results of this research, YOLOv5 is essential, even though its accuracy rate is slightly lower than Faster RCNN. In actual TSR, a faster recognition speed is frequently needed. TSR should use YOLOv5.