基于图像识别的液压支架护帮板收回状态监测方法
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摘要
针对现有接触式液压支架护帮板状态监测方法在矿井雾尘环境下应用存在故障率高、测量结果容易受机身倾斜等因素影响等问题,提出了一种基于图像识别的液压支架护帮板收回状态监测方法。该方法利用雾尘图像清晰化算法与机器视觉测量方法对液压支架护帮板的收回角度进行监测,通过测量护帮板角度来确定液压支架护帮板的收回状态。首先采用改进的暗通道先验算法与导向滤波多尺度Retinex算法对采集的图像进行去雾处理,对经去雾处理的图像进行小波融合,着重恢复雾尘图像的边缘细节信息;然后利用机器视觉测量方法对融合图像的感兴趣区域(ROI)进行提取、二值化、水平和垂直投影处理,提取骨架、骨架像素点,拟合生成直线,由已标定好的CCD相机进行坐标变换,输出护帮板真实角度,进而判断护帮板是否收回。实验结果表明,该方法实现了煤矿井下雾尘图像的清晰化处理,保留了图像细节,且测量精确度高,综合误差小于2°,满足对护帮板的监测要求。
In view of problems of high failure rate and easy to be affected by incline of shearer in the application of contact-type monitoring method of hydraulic support in environment of mine fog and dust,a monitoring method of recovery stae of hydraulic support guard plate based on image recognition was proposed.The method uses fog dust image sharpening algorithm and machine vision measurement method to carry out monitoring of recovery angle of the guard plate of the hydraulic support,and determines the recovery state of the guard plate of the hydraulic support by measuring the angle of the guard plate.Firstly,an improved dark channel prior algorithm and a multi-scale Retinex algorithm with guided filteringare adopted to defog the captured image,and then wavelet fusion is carried out on the defogging image,focusing on restoring the edge details of the image of fog and dust.Then,the region of interest(ROI)of the fusion image is extracted,binarized and processed by horizontal and vertical projection with machine vision measurement method,the skeleton and skeleton pixel points are extracted and generated into straight lines by fitting,coordinate transformation is carried out by the calibrated CCD camera to output true angle of the guard plate,so as to judge whether the guard plate is recovered.The experimental results show that the method realizes sharpening process of images with fog and dust in underground coal mine,and keep the detail of the image,and has accurate measurement result,and the synthetic error is less than2°,which meets monitoring requirements for the guard plate.
In view of problems of high failure rate and easy to be affected by incline of shearer in the application of contact-type monitoring method of hydraulic support in environment of mine fog and dust,a monitoring method of recovery stae of hydraulic support guard plate based on image recognition was proposed.The method uses fog dust image sharpening algorithm and machine vision measurement method to carry out monitoring of recovery angle of the guard plate of the hydraulic support,and determines the recovery state of the guard plate of the hydraulic support by measuring the angle of the guard plate.Firstly,an improved dark channel prior algorithm and a multi-scale Retinex algorithm with guided filteringare adopted to defog the captured image,and then wavelet fusion is carried out on the defogging image,focusing on restoring the edge details of the image of fog and dust.Then,the region of interest(ROI)of the fusion image is extracted,binarized and processed by horizontal and vertical projection with machine vision measurement method,the skeleton and skeleton pixel points are extracted and generated into straight lines by fitting,coordinate transformation is carried out by the calibrated CCD camera to output true angle of the guard plate,so as to judge whether the guard plate is recovered.The experimental results show that the method realizes sharpening process of images with fog and dust in underground coal mine,and keep the detail of the image,and has accurate measurement result,and the synthetic error is less than2°,which meets monitoring requirements for the guard plate.
引文
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[17]崔旭东,杨有.结合HE和改进MSRCR的交通雾霾图像增强[J].重庆师范大学学报(自然科学版),2018(1):100-106.CUI Xudong, YANG You. Traffic haze image enhancement combined with HE and improved MSRCR[J].Journal of Chongqing Normal University(Natural Science),2018(1):100-106.
[18]张肃,徐春云,王文生.基于多小波增强的雾天运动目标跟踪技术[J].红外与激光工程,2014,43(2):625-632.ZHANG Su, XU Chunyun, WANG Wensheng.Tracking technology of moving target in foggy weather based on multi-wavelet enhancement[J].Infrared and Laser Engineering,2014,43(2):625-632.
[19]郭卫,李红卫,王渊,等.低照度环境下采煤机摇臂角度测量方法[J].工矿自动化,2018,44(5):47-51.GUO Wei, LI Hongwei, WANG Yuan,et al.Rocker angle measurement method of shearer under low illumination environment[J].Industry and Mine Automation,2018,44(5):47-51.
[20] NICOLAS H,JEAN-PHILIPPE T,DIDIER A,et al.Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image Analysis and Stereology,2011,27(2):87-95.
[2]魏文艳,刘清.一种用于保护矿井下综采工作面支架护帮板的自动控制系统和自动控制方法:201210279735.3[P].2013-01-02.
[3]徐勇智.液压支架护帮板收放监测系统研究[D].西安:西安科技大学,2016,1-52.
[4]梁海权,王新军,黄金福,等.矿用液压支架状态监测装置及测量方法:201310451563.8[P].2014-01-15.
[5] MACK C A.Fifty years of moore's law[J].IEEE TransactionsonSemiconductorManufacturing,2011,24(2):202-207.
[6] CHAPERON T, DROULEZ J, THIBAULT G.Reliable camera pose and calibration from a small set of point and line correspondences:a probabilistic approach[J]. ComputerVision&Image Understanding,2011,115(5):576-585.
[7]吴迪,朱青松.图像去雾的最新研究进展[J].自动化学报,2015,41(2):221-239.WU Di, ZHU Qingsong. The latest research progress of image dehazing[J].Acta Automatica Sinica,2015,41(2):221-239.
[8]智宁,毛善君,李梅.基于照度调整的矿井非均匀照度视频图像增强算法[J].煤炭学报,2017,42(8):2192-2199.ZHI Ning,MAO Shanjun,LI Mei.Enhancement algorithm based on illumination adjustment for nonuniform illuminance video images in coal mine[J].Journal of China Coal Society, 2017,42(8):2190-2197.
[9] HE K,SUN J,TANG X.Single image haze removal using dark channel prior[C]//IEEE Conference on Computer Vision and Pattern Recognition,2009:1956-1963.
[10] HE K,SUN J,TANG X.Guided image filtering[M].Berlin Heidelberg:Springer,2010:1397-1409.
[11]刘国,吕群波,刘扬阳.基于自适应暗原色的单幅图像去雾算法[J].光子学报,2018,47(2):173-180.LIU Guo,LYU Qunbo, LIU Yangyang. Single image dehazing algorithm based on adaptive dark channelprior[J]. ActaPhotonicaSinica,2018,47(2):173-180.
[12]谢伟,余瑾,涂志刚,等.消除光晕效应和保持细节信息的图像快速去雾算法[J/OL].计算机应用研究,2019,36(5).[2018-04-18].http//www.arocmag.com/article/02-2019-05-058.html.XIE Wei, YU Jin, TU Zhigang, et al. Fast algorithm for image defogging by eliminating Halo effect and preserving details[J/OL]. Application Research of Computers,2019,36(5).[2018-04-18].http//www.arocmag.com/article/02-2019-05-058.html.
[13]杜明本,陈立潮,潘理虎.基于暗原色理论和自适应双边滤波的煤矿雾尘图像增强算法[J].计算机应用,2015,35(5):1435-1438.DU Mingben, CHENLichao, PANLihu.Enhancement algorithm for fog and dust images in coal mine based on dark channel prior theory and bilateral adaptive filter[J].Journal of Computer Application,2015,35(5):1435-1438.
[14]方帅,杨静荣,曹洋,等.图像引导滤波的局部多尺度Retinex算法[J].中国图象图形学报,2012,17(7):11-18.FANG Shuai,YANG Jingrong,CAO Yang,et al.Local multi-scale Retinex algorithm based on guided image filtering[J].Journal of Image and Graphics,2012,17(7):11-18.
[15]汤群芳,杨杰,刘海波,等.基于暗通道先验的单幅图像快速去雾方法[J].光子学报,2017,46(9):205-213.TANG Qunfang,YANG Jie,LIU Haibo,et al.Fast single-image dehazing method based on dark channel prior[J]. Acta Photonica Sinica,2017,46(9):205-213.
[16]陈书贞,任占广,练秋生.基于改进暗通道和导向滤波的单幅图像去雾算法[J].自动化学报,2016,42(3):455-465.CHEN Shuzhen,REN Zhanguang,LIAN Qiusheng.Single image dehazing algorithm based on improved dark channel prior and guided filter[J]. Acta Automatica Sinica,2016,42(3):455-465.
[17]崔旭东,杨有.结合HE和改进MSRCR的交通雾霾图像增强[J].重庆师范大学学报(自然科学版),2018(1):100-106.CUI Xudong, YANG You. Traffic haze image enhancement combined with HE and improved MSRCR[J].Journal of Chongqing Normal University(Natural Science),2018(1):100-106.
[18]张肃,徐春云,王文生.基于多小波增强的雾天运动目标跟踪技术[J].红外与激光工程,2014,43(2):625-632.ZHANG Su, XU Chunyun, WANG Wensheng.Tracking technology of moving target in foggy weather based on multi-wavelet enhancement[J].Infrared and Laser Engineering,2014,43(2):625-632.
[19]郭卫,李红卫,王渊,等.低照度环境下采煤机摇臂角度测量方法[J].工矿自动化,2018,44(5):47-51.GUO Wei, LI Hongwei, WANG Yuan,et al.Rocker angle measurement method of shearer under low illumination environment[J].Industry and Mine Automation,2018,44(5):47-51.
[20] NICOLAS H,JEAN-PHILIPPE T,DIDIER A,et al.Blind contrast enhancement assessment by gradient ratioing at visible edges[J].Image Analysis and Stereology,2011,27(2):87-95.