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熊显名, 肖青山. 基于图像处理的隧道洞口自动识别与应用[J]. 桂林电子科技大学学报, 2018, 38(4): 308-312.
引用本文: 熊显名, 肖青山. 基于图像处理的隧道洞口自动识别与应用[J]. 桂林电子科技大学学报, 2018, 38(4): 308-312.
XIONG Xianming, XIAO Qingshan. Automatic recognition and application of tunnel hole based on image processing[J]. Journal of Guilin University of Electronic Technology, 2018, 38(4): 308-312.
Citation: XIONG Xianming, XIAO Qingshan. Automatic recognition and application of tunnel hole based on image processing[J]. Journal of Guilin University of Electronic Technology, 2018, 38(4): 308-312.

基于图像处理的隧道洞口自动识别与应用

Automatic recognition and application of tunnel hole based on image processing

  • 摘要: 针对隧道洞外亮度测量时隧道洞口居中的圆形目标区域受自然环境影响发生偏移,导致根据当前圆形区域的灰度信息得到的隧道洞外亮度值不准确的问题,提出了一种基于图像处理的方法。让微处理器重新自动识别出隧道洞口并选定圆形目标区域,使测得的亮度值恢复正常。该方法利用相机采集图像,让微处理器(树莓派)对采集的图片采用图像处理技术自动识别出隧道洞口,选定隧道洞口居中的圆形目标区域,使根据圆形目标区域灰度值得到的洞外亮度值恢复正常。结果表明,该方法能准确识别出隧道洞口,目前已成功应用在广西境内合那高速猪谷山隧道。

     

    Abstract: The circular target tunnel accesszone luminance measurement center of the tunnel affected by the natural environment caused by gray information of the current circular area of the tunnel accesszone luminance value is not accurate, a method of automatic identification of tunnel entrance based on image processing was proposed. The microprocessor can identify the tunnel hole and select the circular target areaautomatically, whichcan make the measured brightness value return to normal. The image was collected with camera in this method. And then image processing technologies were used to automatically identify the tunnel entrance by using a microprocessor (raspberry pie).Finally, the outside tunnel brightness value returned to normal according to the gray value of the circular target area when the circular target area of the tunnel hole was selected. The results show that this method can identify the tunnel hole accurately and which has been used in the high-speed pig Valley Tunnel of Guangxi successfully.

     

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