Computer Vision and the IoT-Based Intelligent Road Lane Detection System

Abstract

Many technical improvements have recently been made in the field of road safety, as accidents have been increasing at an alarming rate, and one of the major causes of such accidents is a driver’s lack of attention. To lower the incidence of accidents and keep safe, technological innovations should be made. One way to accomplish this is with IoT-based lane detection systems, which function by recognizing the lane borders on the road and then prompting the turning of the road. Because of the various road conditions that one can encounter when driving, lane detection is a difficult problem. An image processing-based method for lane detection has been proposed in this paper. In this regard, each frame from the video is extracted and image processing techniques are applied for the detection of lanes. The frame which is extracted from the video is then subjected to a Gaussian filter for the removal of noise. Subsequently, color masking has been used to process the frame to detect only the road lanes, whose edges are obtained by applying the canny edge detection algorithm. Afterward, the Hough transform has been applied to the region of interest to extrapolate the lines. Finally, the path is plotted along the lines, and turns are predicted by using the concept of vanishing points.

Detect Lane Line for Self-Driving Car Using Hue Saturation Lightness and Hue Saturation Value Color Transformation

“…The presence of multiple dispersed shadow regions can lead to erroneous lane line detections by the algorithm of lane line detection, as shown in previous researches [21,22,24]. Therefore, we want to solve this limitation by the lane line detection algorithm to detect accurately in dispersed shadow regions.…”

“…The system utilizes the Hough transform technique to determine the lines in the outcomes of the segmentation network. Rudregowda, et al [22] presented a method for detecting lanes using image processing techniques. The video is subjected to frame extraction and image processing techniques to identify the lanes in this context.…”