engleski

Adaptive Image Processing Technique for Quality Control in Ceramic Tile Production

Automation of the visual inspection for quality control in production of materials with textures (tiles, textile, leather, etc.) is not widely implemented. A sophisticated system for image acquisition, as well as a fast and efficient procedure for texture analysis is needed for this purpose. In this paper the Surface Failure Detection (SFD) algorithm for quality control in ceramic tiles production is presented. It is based on Discrete Wavelet Transform (DWT) and Probabilistic Neural Networks (PNN) with radial basis. DWT provides a multi-resolution analysis, which mimics behavior of a human visual system and extracts from the tile image features important for failure detection. Neural networks are used for classification of tiles with respect to presence of defects. Classification efficiency mainly depends on the proper choice of training vectors for neural networks. For neural networks preparation we propose an automated adaptive technique based on statistics of the tile defects textures. This technique enables fast adaptation of the SFD algorithm to different textures, which is important for automated visual inspection in the production of a new tile type.

automated visual inspection; quality control in tile production; discrete wavelet transform; probabilistic neural network

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