Study On Wood Defects Testing Based On Artificial Neural Network

X-ray was adopted as a measure method for log nondestructive testing. The difference of X-ray intensity after exposure was tested in order to judge whether the defect of log exist or not. At the other side of log, image enhancement device was used to receive the log image, and then via low-light camera transmit the X-ray log image which was transformed from analog image to digit image by A/D converter to the computer memory. MATLAB and VC++ image processing program were applied to process and analyze the image of log with defects. The characters of image defects were extracted to identify the size and position of defects in a log. In this paper, three common defects which are knot, grub-hole and rot were studied.On the base of signals processing of nondestructive testing and characteristic construction, characteristic parameters were applied to establish the mathematic model of defects recognition, especially for the character of nondestructive testing. ANN(Artificial neural network) was established by selecting multi-BP networks, the characteristic parameter for network recognition could reflect all characters of log defects. Defect gray averaging, defect gray variance and ratio of the defect length and width were served as three parameter inputs for ANN recognition, and the network was trained by using BP network algorithm. BP algorithm was applied to trained samples, and weight-coefficient of neuron was adjusted in different layers by BP algorithm, input all sample sequences repeatedly until all the weight-coefficient no longer change and the error was in the fixed scope. After studying network, coefficient matrix of each unit which includes input layer, intermediate layer and output layer was gained. MATLAB was applied to simulate trained ANN, then the input vector model was gained, and network recognition was completed. Based on character acquisition, artificial neural network was adopted to recognize the kind of log defects effectively and the interior defects information of log was judged correctly. The experimental results show that ANN is an effective method for the nondestructive testing and classifying of three defects. This method can be used in other log defects nondestructive testing and classifying.