王星, 白尚旺, 潘理虎, 陈立潮, 张英俊. 基于计算机视觉的带式输送机跑偏监测[J]. 煤矿安全, 2017, 48(5): 130-133.
    引用本文: 王星, 白尚旺, 潘理虎, 陈立潮, 张英俊. 基于计算机视觉的带式输送机跑偏监测[J]. 煤矿安全, 2017, 48(5): 130-133.
    WANG Xing, BAI Shangwang, PAN Lihu, CHEN Lichao, ZHANG Yingjun. Deviation Monitoring of Belt Conveyor Based on Computer Vision[J]. Safety in Coal Mines, 2017, 48(5): 130-133.
    Citation: WANG Xing, BAI Shangwang, PAN Lihu, CHEN Lichao, ZHANG Yingjun. Deviation Monitoring of Belt Conveyor Based on Computer Vision[J]. Safety in Coal Mines, 2017, 48(5): 130-133.

    基于计算机视觉的带式输送机跑偏监测

    Deviation Monitoring of Belt Conveyor Based on Computer Vision

    • 摘要: 针对带式输送机胶带在运行过程中易出现跑偏的情况,提出了一种基于计算机视觉的输送带跑偏监测方法。首先将视频监控中采集到的视频图像设置感兴趣区域(Region of Interest, ROI)以减少计算量,同时对ROI进行图像预处理。然后采用改进的Canny边缘检测算法得到ROI边缘二值图像,利用累计概率霍夫变换(Progressive Probabilistic Hough Transform ,PPHT)提取输送带边缘直线特征,最后根据所得直线特征来判断输送带是否跑偏。

       

      Abstract: For the situation that conveyor belt is easily deviated during operation, the belt deviation monitoring method based on the computer vision is proposed. Firstly, the Region of Interest (ROI) in video surveillance is set to reduce the amount of calculation, and the image preprocessing is performed on the ROI. Then the improved Canny edge detection algorithm is used to get the ROI edge binary image, and the linear feature of conveyor belt edge is extracted by using Progressive Probabilistic Hough Transform (PPHT). Finally, we determine whether the conveyor belt is deviated or not according to the linear features.

       

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