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Design of Fuel Detection System in Transparent Pipe Using Image Processing Method Based on Raspberry-Pi

机译:基于覆盆子 - PI的图像处理方法透明管燃料检测系统设计

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Delivering process of liquid fuels from refinery units into storage plants is no longer using transportation (trains ortrucks) because it is so costly. So pipes are used for this delivering process. Densitometer is used to determine the timewhen valve close or open manually for guiding each fuel to each storage tank. Density parameter is not suitable to be themain parameter because the densities of each oil are almost the same. Therefore it needs to design an innovation ofRaspberry-Pi's liquid fuel detection system to improve detection accuracy. in this research, fuel color data acquisitionprocess on a transparent pipe on HSV color space (hue, saturation, value) are done by webcam then data processed byRaspberry - Pi to define the fuel type. Script programming has written in Python 2.7 and image processing is done withOpenCV library. Optimal intensity lighting at 25 lux and minimum at 8 lux. Each fuel and interface can be well known.The values, accuracy, standard deviation of hue and saturation readings respectively for pertamax are 98 - 100, 98.8%,0,3 and 120 - 140, 98.48%, 3,07, for diesel are 19 - 25, 99.12%, 0,41 and 110 - 130, 97.35%, 3,79, and for kerosene are92 – 100 , 98.93%, 1,38 and 30 - 45, 92.55%, 4,63. Determining the fuel concentration on the interface can’t be donebecause there is the same value of HSV bit.
机译:将液体燃料从炼油装置运送到储存厂的过程不再使用运输(列车或卡车)因为它是如此昂贵。所以管道用于该交付过程。密度计用于确定时间当阀门关闭或手动打开时,将每个燃料引导到每个储罐。密度参数不适合成为主要参数,因为每个油的密度几乎相同。因此,它需要设计创新覆盆子 - PI的液体燃料检测系统提高检测精度。在本研究中,燃料颜色数据采集在HSV颜色空间(色调,饱和度,值)上的透明管道的过程由网络摄像头完成,然后通过覆盆子 - PI定义燃料类型。脚本编程已写入Python 2.7,并使用图像处理OpenCV库。在8勒克斯的最佳强度照明和8勒克斯的最低限度。每个燃料和界面都可以是众所周知的。分别为Pertamax的色调和饱和度读数的值,准确度,标准偏差为98-100,98.8%,0.3和120-140,98.48%,3,07,柴油为19-25,99.12%,0,41和110-130,97.35%,3,79和煤油92-100,98.93%,1,38和30-45,92.55%,4,63。确定界面上的燃油浓度无法完成因为HSV位的值相同。

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