An automatic inspection system of printed art tile defects is reported in this artucke. After calculating eight defect features of art tile using the gray level co-occurrence matrix and the average R, G, B values of a defective area, the results were input into a backward propagation neural network for training the defect classifier. During inspection, the proposed system compared the inspected image with a standard image and removed noise by an erosion operation in order to preliminarily determine whether the art tile had defects. For the defective art tile images, the proposed classifier successfully identified four types of common printing defects. The proposed algorithm had an average recognition rate of 90%, suggesting that the recognition accuracy is good, and only requires 1-2 s to inspect an art tile with the size of 15 × 15 cm2. The inspection speed is faster than the conventional manual inspection, and recognition results are more stable. The proposed system can reduce the risk of error caused by the long duration of manual inspection, and thus, can reduce manufacturing costs.
- defect classifier
- gray level co-occurrence matrix
- printed art tile