Research Of Temperature Measurement Based On Neural Network And Image Color

Temperature is a physical quantity which we often use in scientific research, industrial production and daily life. With the improvement of automation degree and product quality requirements, the demand for accurately and rapidly temperature measurement and conformity that also increase, so temperature measurement has became one of the most important topics in scientific research. Especially the measurement of high-temperature objects, for it is very difficult to achieve, which has attracted the attention at home and abroad. In the past, we generally used the contact sensor for the measurement of high temperature objects, such as thermocouple, thermal resestance, optical pyrometer and so on. But these traditional sensors are difficult to abtain the real-time temperature that we need and have low accuracy. In addition, the harsh environment of industrial site will also limit the use of some measuring instruments and meters with high accuracy. Therefore, we put into the research of temperature measurement that based on neural network and image color.As the neural network can fit the nonlinear well, while it is existing in a relationship between the color of objects with high temperature and it's temperature, so that we can use cameras to collect color images of objects with high temperature at different temperatures, after that we use nonlinear approximation capability of neural networks to fit the nonlinear relationship between color and temperature.We first compare the color models to establish a suitable model, that is the RGB model, and preprocess the color images collected to get the color feature values we need. The image preprocessing is implemented in the MATLAB platform, including image smoothing and image segmentation. Then train BP, RBF and wavelet networks with the color feature values as input of neural network. In the meanwhile, we select the hidden nodes, learning rate for BP network and expansion coefficient for RBF network. The analysis of experimental data showes that the temperature measurement based on neural network technology and image color is very feasible, and also analyzed and compared three kinds of neural networks from the convergent speed,the training error and limitations. RBF network can be found for the other two networks has higher training speed,smaller error and more stable. It can be used in the temperature measurement system based on image colors.