Lane Detection and Distance Estimation Using Computer Vision Techniques

In the realm of computer vision, the term "autonomous driving" has become a buzzword. The main goal of the autonomous driving is to reduce human efforts while driving. However, dealing with measurements of distance raises numerous obstacles, both in terms of equipment and approach. The use of cameras to measure the distance of an object is practical and popular for obstacle avoidance and navigation.. This work focuses on vehicle distance measuring of traffic signs and cars, which is a critical task in the image processing domain. In this research, the suggested system employs two cameras installed in the hosting vehicle in front, to obtain the data and estimate distance. The proposed pipeline starts with YOLO v3 and YOLOv2 algorithms for detecting traffic signs and cars in the video frames. The distances of the detected objects are measured using triangle similarity approach. In final phase, lane segmentation and grid marking are added along with these results. As a result, it will assist drivers inmaking decisions prior to reaching signs, potentially resulting in improved safety decisions.