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@article{150461,
        author = {Omkar Panchal and Tejas Shinde and Akshada Tupe and Shraddha Bhagwat and Prof.D. S. Shingate},
        title = {Autonomous Road Sign Recognition and Lane Detection Using Convolutional Neural Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {6},
        pages = {160-163},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=150461},
        abstract = {Approximately 1.35 million people die each year as a result of road traffic crashes, and between 40 to 70 million are injured drastically. Most of these accidents takes place due to lack of response time to instant traffic events. To design such recognition and detection system in autonomous cars, it is important to monitor and guide through real time traffic events. This involves 1) Road sign recognition 2) Road lane detection. Road sign recognition have been studied for many years and with many good results, but road lane detection is a less-studied field. Road lane detection provide drivers with very valuable information about which lane they are following and any possible lane departure, in order to make driving safer and easier. In this paper, an attempt is made to develop such system, by applying image recognition to capture traffic signs, classify and process them correctly using Convolutional Neural Network.},
        keywords = {Computer Vision , Canny Detection , Neural Networks , Gussian filters.},
        month = {},
        }