Research on recognition of wood defect types based on back-propagation neural network

Contrasting to the original method of identifying the types of wood defects which requires the experienced technical staff with good discrimination to consider the characteristics of wood defects in the image, this paper presents a new method which can identify the types of internal wood defects rapidly and accurately by BP neural network which can analyse the visual characteristics parameters of wood defects extracted from the wood digital image. It analyses the results that different network structure and network parameters impact the capability of wood defects classification, presents the best parameters of BP neural networks which is used to identify the types of wood defects. This paper presents the way of extracting the wood defect characteristics and the way of processing the wood digital image in which has the visual flaw such as noise and low contrast.