基于视觉传感器的异纤在线检测技术研究
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摘要
探讨基于CCD视觉传感器的异纤在线检测技术。为了提高异纤的检出率,对检测系统的整体结构进行设计,采用中值滤波的方法对灰度处理后的图像进行去噪处理,利用边缘检测的方法实现对异性纤维的识别,获取异纤的边界信息,实现对目标的定位;通过PLC控制检测系统和异性纤维气动清除装置,实现对异纤准确且及时的检测与清除。通过试验可得出装置的最佳参数:CCD摄像机检测距离110 mm,检测原棉层厚度12 mm,待检原棉层移动速度2.0m/min,剥棉罗拉转速1 000r/min,喷嘴压力0.5MPa。认为:异性纤维在线检测装置检测能力较高,对普通异纤的检出率可以达到90%以上,对头发丝等细小物质的检出率可以达到85%以上。
The online inspection technology for foreign fibers based on CCD visual sensor was discussed.In order to improve the detection rate of foreign fibers,the whole structure of detection system was designed.The median filter method was used for the de-noising treatment of images after grey processing.The edge detection method was adopted to achieve the identification of foreign fiber,to obtain the boundary information of foreign fiber and to realize the target location.Through PLC control detecting system and the pneumatic clearing device for foreign fiber,the foreign fibers were detected and cleaned accurately and timely.The optimizing parameters of the equipment were obtained by experiments.The detecting distance for CCD video camera is 110 mm,the raw cotton layer thickness in detection is 12 mm,the moving speed for the raw cotton layer waiting for inspection is2.0 m/min,the stripper roller running speed is 1 000 r/min and the nozzle pressure is 0.5 MPa.It is considered that the detectability for foreign fiber with the online detection equipment is higher.Its detection rate for normal foreign fibers can be reached larger than 90% and the detection rate for tiny substantial like hair and so on can be reached larger than 85%.
The online inspection technology for foreign fibers based on CCD visual sensor was discussed.In order to improve the detection rate of foreign fibers,the whole structure of detection system was designed.The median filter method was used for the de-noising treatment of images after grey processing.The edge detection method was adopted to achieve the identification of foreign fiber,to obtain the boundary information of foreign fiber and to realize the target location.Through PLC control detecting system and the pneumatic clearing device for foreign fiber,the foreign fibers were detected and cleaned accurately and timely.The optimizing parameters of the equipment were obtained by experiments.The detecting distance for CCD video camera is 110 mm,the raw cotton layer thickness in detection is 12 mm,the moving speed for the raw cotton layer waiting for inspection is2.0 m/min,the stripper roller running speed is 1 000 r/min and the nozzle pressure is 0.5 MPa.It is considered that the detectability for foreign fiber with the online detection equipment is higher.Its detection rate for normal foreign fibers can be reached larger than 90% and the detection rate for tiny substantial like hair and so on can be reached larger than 85%.
引文
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[2]师红宇,管声启.基于视觉注意计算模型的棉花异性纤维检测[J].东华大学学报(自然科学版),2016,42(3):400-405.
[3]李鑫,章友鹤.关于棉纺厂异纤检测和清除设备使用效果与发展前景分析[J].现代纺织技术,2010,18(2):39-43.
[4]周择旭.异纤在线检测与清除关键技术的研究[D].天津:天津工业大学,2012.
[5]王欣.基于机器视觉的棉花伪异性纤维识别研究[D].北京:中国农业大学,2015.
[6]张晓,苗兴华.对棉花杂质检验的思考[J].中国棉花加工,2000(1):29-30.
[7]高伟,王志衡,赵训坡,等.基于HSI颜色空间的棉花杂质高速实时检测方法[J].自动化学报,2008,34(7):729-735.
[8]MUSTAFIC A,LI C,HAIDEKKER M.Blue and UV LED-induced Fluorescence in Cotton Foreign Matter[J].Journal of Biological Engineering,2014,8(1):29-33.
[9]林昌建.基于光电传感的棉花产量在线监测系统研究[D].上海:上海交通大学,2013.
[10]阎磊,郝爱萍,王志鸿.异纤的危害及清除方法探讨(待续)[J].棉纺织技术,2011,39(11):25-28.
[11]王欣,李道亮,杨文柱,等.基于可见光机器视觉的棉花伪异性纤维识别方法[J].农业机械学报,2015,46(8):7-14.
[12]YANG W,LU S,WANG S,et al.Fast Recognition of Foreign Fibers in Cotton Lint Using Machine Vision[J].Mathematical&Computer Modelling,2011,54(3/4):877-882.
[13]ZHANG H,LI D.Applications of Computer Vision Techniques to Cotton Foreign Matter Inspection:A Review[J].Computers&Electronics in Agriculture,2014(109):59-70.
[14]王雪松,戴琼海,焦李成,等.高性能探测成像与识别的研究进展及展望[J].中国科学:信息科学,2016,46(9):1211-1235.
[15]陈长风,黄萍萍,王建华.自适应灰度门限法在水面图像分割中的应用[J].现代电子技术,2011,34(12):121-124.
[16]吴晓光,王涤琼,盛慧.一种获取图像区域最小外接矩形的算法及实现[J].计算机工程,2004,30(12):124-125.
[17]MIEHAEL W H,WILLIAM E H.Relaxation Method for Supervised Image Segmentation[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(9):946-962.