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输送带跑偏检测方法研究

王锴 曾祥进 黎新 张锐 徐成

王锴,曾祥进,黎新,等. 输送带跑偏检测方法研究[J]. 工矿自动化,2023,49(3):23-30, 52.  doi: 10.13272/j.issn.1671-251x.2022050064
引用本文: 王锴,曾祥进,黎新,等. 输送带跑偏检测方法研究[J]. 工矿自动化,2023,49(3):23-30, 52.  doi: 10.13272/j.issn.1671-251x.2022050064
WANG Kai, ZENG Xiangjin, LI Xin, et al. Research on conveyor belt deviation detection method[J]. Journal of Mine Automation,2023,49(3):23-30, 52.  doi: 10.13272/j.issn.1671-251x.2022050064
Citation: WANG Kai, ZENG Xiangjin, LI Xin, et al. Research on conveyor belt deviation detection method[J]. Journal of Mine Automation,2023,49(3):23-30, 52.  doi: 10.13272/j.issn.1671-251x.2022050064

输送带跑偏检测方法研究

doi: 10.13272/j.issn.1671-251x.2022050064
基金项目: 国家自然科学基金资助项目(61502355);湖北省三峡实验室创新基金资助项目(SC215001);武汉工程大学研究生教育创新基金资助项目(CX2021246)。
详细信息
    作者简介:

    王锴(1997—),男,山东菏泽人,硕士研究生,研究方向为图像处理和计算机视觉,E-mail:1181640871@qq.com

  • 中图分类号: TD634

Research on conveyor belt deviation detection method

  • 摘要: 基于机器视觉的输送带跑偏检测方法检测的输送带边缘特征中包含伪边缘,现有研究难以识别伪边缘,且多场景适应性差。针对该问题,对输送带监控图像提取感兴趣区域(ROI)并进行归一化,采用较大阈值区间的Canny算法提取边缘特征点,以提高算法的场景适应性,并采用形态学滤波方法处理部分杂质及伪边缘;对于Canny算法无法检测到有效边缘的图像,对提取的ROI进行伽马变换和45,135° 方向的梯度滤波,以增强边缘特征,之后进行基于Canny算法的特征点提取和形态学滤波。以边缘点像素值关系、邻域特征、紧密性特征,以及边缘线长度、相对位置、斜率等作为约束条件,采用基于分治搜索思想的直线筛选排序算法对提取的边缘特征点进行筛选及拟合,得到输送带实时边缘。将实时边缘的像素值与未发生跑偏时边缘像素值做差,得到当前跑偏的像素值。试验结果表明,针对多种场景下的输送带监控图像,基于Canny算法和直线筛选排序的输送带跑偏检测方法检测误差小于3个像素值,百张图像检测时间为6.945 1 s,边缘计算机处理4路视频图像的CPU占有率为132%,满足现场输送带边缘检测的准确性、实时性要求。

     

  • 图  1  输送带跑偏检测流程

    Figure  1.  Flow of conveyor belt deviation detection

    图  2  输送带图像预处理结果

    Figure  2.  Pretreatment results of conveyor belt image

    图  3  输送带图像暗处理效果

    Figure  3.  Processing effect of conveyor belt image in dark environment

    图  4  直线筛选排序算法流程

    Figure  4.  Flow of line filtering and sorting algorithm

    图  5  直线筛选排序算法处理效果

    Figure  5.  Process effect of line filtering and sorting algorithm

    图  6  邻域点结构

    Figure  6.  Neighborhood point structure

    图  7  实际检测出的边缘点

    Figure  7.  Actual detected edge pointss

    图  8  分治搜索流程

    Figure  8.  Flow of divide and conquer search

    图  9  输送带跑偏检测像素偏移值可视化

    Figure  9.  Pixel offset value visualization of conveyor belt deviation detection

    图  10  输送带跑偏检测现场试验

    Figure  10.  Actual operating effect diagram

    表  1  输送带跑偏检测结果

    Table  1.   Conveyor belt deviation detection results

    场景检测正确数/张检测准确率/%漏检数/张漏检率/%
    左边缘右边缘左边缘右边缘
    13 5783 57299.3899.2200
    23 5693 60099.1310000
    33 5633 49399.4497.48170.47
    43 3343 42195.3397.821032.86
    53 4973 50697.9298.17290.81
    63 2002 75296.5683.042867.94
    下载: 导出CSV

    表  2  输送带跑偏检测算法运行时间

    Table  2.   Running time of conveyor belt deviation detection algorithm

    图像数/张1101001 000
    时间/s0.073 90.712 56.945 169.478 6
    下载: 导出CSV

    表  3  边缘计算机处理视频流的CPU占有率

    Table  3.   CPU occupancy of edge computer processing video flow

    视频流/路1234
    CPU占有率/%5281106132
    下载: 导出CSV

    表  4  输送带跑偏检测像素偏移值

    Table  4.   Pixel offset value of conveyor belt deviation detection

    场景实际像素偏移值检测像素偏移值像素差
    左边缘右边缘左边缘右边缘左边缘右边缘
    11015101500
    2232300
    32222232210
    4−30−29−32−2920
    5−18−20−19−2111
    6−20−21−19−2312
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-05-24
  • 修回日期:  2023-03-15
  • 网络出版日期:  2022-09-28

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