Improving Lane Management: Leveraging OpenCV and IoT to Empower Smart Environments

Lane detection is an essential component of autonomous driving systems that allows vehicles to travel the road safely. This study offer a technique to detection using OpenCV, a famous computer vision toolkit, in this research paper. To extract lane boundaries from photos and videos acquired by a camera mounted on a vehicle, this study use a combination of colour space transformation, edge detection, and image thresholding approaches. This study also offers a real time lane recognition system that proves approach’s effectiveness in recognizing lane borders in real-world circumstances. Road safety is a crucial concern as accidents are becoming more frequent due to driver inattention. To decrease accidents & ensure safety, technological advancements need to be made. One important effective method is using Lane Detection Systems, which can detect lane borders on the road and alert drivers if they cross them. Though it is among the most difficult of the elements to be detected, since the condition of the roads keeps changing, especially at night and under different lighting conditions, lane detection is still one of the vital components in an advanced transportation system. Lane boundary detection is where a camera in a vehicle is mounted on the front and images of the roads are acquired with it. In this paper, the methodology to divide the image of a video image into sub-images and the extracted features of the image are applied to identify the lanes. Based on this realization brings to the forefront the proposed techniques in lane marking detection and the ability of the work to enhance the knowledge base further. It is one big challenge in the way to fully autonomous vehicles because the software may not provide accurate detection, which may raise serious security concerns.