Abstract Laser lines emitted by the laser level are mostly detected manually and laser particle and optical effects also bring difficulties on measurement. In this paper, we design a detection system for the five-line laser level and propose a laser line measurement method based on ma- chine vision. Image processing is divided into two stages: in the first stage, we use random sample consensus (RANSAC) algorithm combined with Hough transform to fit the laser axis, which can get its position information. In the second stage, a laser edge extraction method based on conditional random fields (CRFs) is proposed, and the sub-pixel width of laser line is obtained by spline interpolation algorithm. The results confirm that the laser level detection method proposed in this paper can realize the corresponding detection precision and requirement.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Detection Method of Laser Level Line Based on Machine Vision


    Contributors:
    Wang, Xiaozhen (author) / Wang, Haikuan (author) / Yang, Aolei (author) / Fei, Minrui (author) / Shen, Chunfeng (author)


    Publication date :

    2017-01-01


    Size :

    10 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    A Review of Lane Line Detection Technology Based on Machine Vision

    Changfeng, Xiang / Wang, Chen / Yu, Li et al. | Springer Verlag | 2021


    A Review of Lane Line Detection Technology Based on Machine Vision

    Changfeng, Xiang / Wang, Chen / Yu, Li et al. | TIBKAT | 2021


    ADVANCED APPLICATIONS: LASER VISION Lasers sharpen machine vision

    Lerner, E. J. | British Library Online Contents | 2000


    Machine vision detection system

    CHEN XINXIN / ZHANG MIN / YU ZHAOQIN et al. | European Patent Office | 2023

    Free access

    Detection of the Tiredness Level of Drivers Using Machine Vision Techniques

    Jimenez, R. / Prieto, F. / Grisales, V. H. | IEEE | 2011