Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision

The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika