Design of an intelligent wood surface grading system based on computer vision

An intelligent system for wood surface detection is designed, which integrates plate transmission, image acquisition, image recognition and sorting equipment. The convey belt is used to carry plates, CCD is employed to acquire images, the recognition program is written by MFC and Open CV in the touch screen industrial computer, and the solenoid valve is controlled by STM32 according to recognition results. In the image localization process, the integral projection method is used to determine the boundary of the plates. In the color classification, the mean, variance and skewness features of the L*a*b* space are extracted to express color information. In the defect detection, a segmentation method based on texture filling is proposed. The texture of the image is extracted and the background color is used to fade the texture part which can reduce impact of texture’s effect, and then weighted threshold is used to segment the defects. After segmentation, the features of area, the edge gray value, the internal gray value and the length and width ratio are used to express defects. In texture recognition, the texture feature extraction method based on Contourlet transform is proposed. By Contourlet transform, one sub-band, six intermediate frequency sub-bands and eight high frequency sub-bands are obtained. By calculating the mean and variance of the low frequency and intermediate frequency coefficients, a 22-dimension feature vector is obtained with the energy of high frequency coefficient matrix. Finally, a SVM classifier is designed to recognize the color, defect and texture. A total of 300 samples are used in the test experiment, when convey speed is under 1.5 m/s, and the classification rate of color is 100%, the recognition rate of live knot, dead knot and crack are 92.2%, 95.6% and 93.3% respectively, and the recognition rate of radial texture and tangential texture are 93.9% and 92.8% respectively.