Open AccessRoad Sign Recognition and Lane Detection using CNN with OpenCV
Author(s) -
Omkar Panchal
Publication year - 2021
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2021.35752
Subject(s) - computer science , traffic sign , convolutional neural network , traffic sign recognition , artificial intelligence , safer , computer vision , process (computing) , sign (mathematics) , advanced driver assistance systems , visualization , pattern recognition (psychology) , computer security , mathematical analysis , mathematics , operating system
As a result of road traffic crashes, approximately 1.35 million people die each year, and between 40 to 70 million are injured drastically. Most of these accidents occurs because of to lack of response time to instant traffic events. To develop such recognition and detection system in autonomous cars, it is important to monitor and guide driver through real time traffic events. This involves Road sign recognition and road lane detection. In order to make the driving process safer and efficient, a plan is made to design a driver-assistance system with road sign recognition and lane detection features. In this system we have focused on two important aspects, Road sign recognition and lane detection. The process of road sign recognition in a video can be broken into two main areas for research; detection and classification using convolutional neural networks. Road signs will be detected by analysing colour information, which can be red and blue, contained on the images whereas, in classification phase the signs are classified according to their shapes and characteristics. Along with road sign recognition we also focused on Road Lane detection which is one significant method in the visualization-based driver support structure and capable to be used for vehicle guiding and monitoring, road congestion avoidance, crash avoidance.