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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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




    Erscheinungsdatum :

    02.05.2024


    Format / Umfang :

    279991 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Nighttime lane detection and vehicle position determination based on OpenCV

    Chen, Leilei / Liu, Yingshun / Zhang, Weiye | SPIE | 2024


    Lane Line Detection Technology Based on OpenCV for Specific Scenarios

    Liu, Wei / Lu, Cunhao / Zhang, Ning | IEEE | 2022


    OpenCV and Perception

    Blubaugh, David Allen / Harbour, Steven D. / Sears, Benjamin et al. | Springer Verlag | 2022


    Real-Time Traffic Flow Management using OpenCV

    Nanda, Shivangi / Singh, Sunny / Singh, Gurpreet | IEEE | 2023


    Enhancing Road Safety: Reckless Driver Detection via OpenCV in Simulated Environments

    Bhosale, Varun / Shah, Jainam / Doshi, Prem et al. | Springer Verlag | 2024