Abstract
The surface quality assurance check is an important task in industrial production of wooden parts. There are many automated systems applying different methods for preprocessing and recognition/classification of surface textures, but in the most cases these methods cannot produce very high recognition accuracy. This paper aims to propose a method for effective recognition of similar wooden surfaces applying simple preprocessing, recognition and classification stage. The method is based on simultaneously training two different neural networks with surface image histograms and their second derivatives. The combined outputs of these networks give an input training set for a third neural network to make the final decision. The proposed method is tested with image samples of seven similar wooden texture images and shows high recognition accuracy. The results are analyzed, discussed and further research tasks are proposed.
Highlights
Many modern automated inspection systems in wooden industry are developed for inspection of the wooden surface quality
That is the reason to develop effective methods and algorithms aiming high recognition accuracy for different kinds of similar textures when evaluating them in production environment
When investigating recognition and classification of a preliminary known texture classes, more suitable is to apply an adaptive recognition method and a supervised learning scheme, since this method gives the more accurate results. In this instance the best variant is to choose neural networks (NN) because of the good NN capabilities to adapt to changes in the input vector, to set precisely the boundaries between the classes offering high recognition accuracy and fast computations in the recognition phase [5]
Summary
Many modern automated inspection systems in wooden industry are developed for inspection of the wooden surface quality. It is difficult to obtain high classification accuracy in this case because of the high correlated texture parametrical descriptions. In some cases the surface textures have to be recognized in movement, because of the production process specifics. That is the reason to develop effective methods and algorithms aiming high recognition accuracy for different kinds of similar textures when evaluating them in production environment. As well optimal (considering the proportion between classification accuracy, calculation simplicity and cost) methods, software and hardware system solutions have to be sought, suitable for implementation in real time systems. Taking into consideration the above discussion, a method for recognition of similar wooden surfaces applying simple preprocessing, recognition and classification stage is presented. The method is based on preliminary analyzing the correlation between different wood texture descriptions, followed by a simultaneously training two different neural networks with surface image histograms and their second derivatives.
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