Road Lane Line Detection Based on ROI Using Hough Transform Algorithm

Now-a-days technology has become the means of survival. Automotive Sector is also affected by this technology growth. Driver safety is one of the most important concern for the automobile industry. Lack of attention causes the road accidents and may endanger the driver and co-passenger lives at risk. The stats presented by WHO on road accidents shows that approximately 1.35 million people dies annually as a result of the car accidents. And about 20–50 million peoples suffer from non-fatal injuries, but they may cause lifetime disabilities. These road crashes also impact the economy of the countries. Most of the countries suffers 3% of their GDP due to road accidents. The major challenge is to make the technology available in the commercial sector. So various methods and algorithms are introduced to achieve better performance and robustness. One of the major components of autonomous vehicles are road lane detection. Marking the region of interest (ROI) in which car should be driven. Recent advancement in the technology like image processing and deep learning helps in achieving the aim to detect road lane lines. Autonomous cars are now equipped with cameras, radar and LIDAR for tracking roads and track environment. In this paper, road lane line detection problem has been addressed using Open CV library; also, an approach for finding an efficient way for detecting road lanes precisely and more accurately has been proposed. The road images captured by the camera mounted on the vehicle is processed and region of interest is masked. After masking the ROI, it is converted into a pixel matrix using NumPy library. The Hough Transform is applied on the matrix and lanes are detected in between which vehicle runs.