基于数字图像处理的温度检测算法研究

A STUDY OF ALGORITHMS OF TEMPERATURE MEASUREMENT BASED ON DIGITAL IMAGE PROCESSING

介绍了基于数字图像处理的温度检测技术原理。针对神经网络温度计算法在计算煤炉的温度时存在较大误差问题,提出用最小二乘法和改进输入的神经网络法计算温度。这两种算法以彩色CCD摄像机输出的三基色之间的比值为自变量或输入量,更好地体现了比色测温原理,因此能较好地消除在实际测温过程中黑度系数、烟尘和火焰脉动对计算结果带来的影响。通过在煤炉上的实验表明,这两种方法的计算精度均高于原神经网络法的精度,而改进输入的神经网络温度算法的计算精度略高于最小二乘法的计算精度,而最小二乘法更加简单实用。

A technology for measuring temperature based on digital image processing is presented. In order to diminish the error of neural-network calculation during testing the temperature inside a coal-fired furnace, two methods are developed in the paper. One is least squares method and the other is improved neural-network method whose inputs is different from the old neural network method. According to colorimetric temperature-measurement algorithm, the ratios among the three basic colors of CCD camera are used as the inputs of both methods, which could be helpful in reducing the influence of emissivity, soot and flame frequency. The results of experiments on a coal-fired furnace show that both algorithms are more precise than the old neural network method. And the improved neural-network method is more accurate then the least squares method, but the latter is more simple and practical.