Adaptive coal flow detection method of belt conveyor
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摘要: 针对现有带式输送机煤流量检测方法存在检测精度易受环境影响、实现过程复杂、信息提取耗时较长等问题，提出了一种基于机器视觉的带式输送机煤流量自适应检测方法。首先，采用基于小波变换的融合算法对带式输送机运输煤料原始图像进行增强处理，并采用OTSU算法将增强图像分割为胶带图像和煤料图像；然后，对煤料图像进行空洞填充、轮廓检测和面积计算等处理，获取煤料图像面积信息；最后，采用基于数学建模的煤流量检测算法，通过计算煤料瞬时体积获得煤流量检测值。试验结果表明，该方法平均检测时间约为30 ms，检测结果与电子胶带秤测量结果的误差约为5%，满足带式输送机自动调速控制系统对煤流量检测实时性和准确性的要求。Abstract: For problems of existing coal flow detection methods of belt conveyor such as susceptibility of detection accuracy to environment, complex realization process, long time-consumption of information extraction and so on, an adaptive coal flow detection method of belt conveyor based on machine vision was proposed. Firstly, the original coal transportation image of belt conveyor is enhanced by a fusion algorithm based on wavelet transform and segmented by OTSU algorithm into belt image and coal image. Secondly, the segmented coal image is processed by cavity filling, contour detection and area calculation to obtain area information of the coal image. Finally, a coal flow detection algorithm based on mathematical modeling is used to obtain coal flow detection value through calculating transient volume of coal. The test results show that the average detection time of the method is about 30 ms, and error between detection results and the measurement ones of electronic belt scale is about 5%, which meets real-time and accuracy requirements for coal flow detection of automatic speed control system of belt conveyor.
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