A Traffic Sign Classifier Model using Sage Maker

Driver assistance technologies that relieve the drivers task, as well as intelligent autonomous vehicles, rely on traffic sign recognition. Normally the classification of traffic signs is a critical challenge for self driving cars. For the classification of traffic sign images, a Deep Network known as LeNet will be used in this study. There are forty three different categories of images in the dataset. There are two aspects to this structure Traffic sign identification and Traffic sign classification. ADASs are designed to perform a variety of tasks, including communications, detection of road markings, recognition of road signs, and detection of pedestrians. There are two aspects to this structure Traffic sign identification and Traffic sign classification. In the methodologies for detecting and recognizing traffic signals various techniques, such as colour segmentation and the RGB to HSI model area unit, were applied for traffic sign detection and recognition. Different elements contribute to recognition of HOG. Arpit Seth | Vijayakumar A "A Traffic Sign Classifier Model using Sage Maker" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42411.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42411/a-traffic-sign-classifier-model-using-sage-maker/arpit-seth