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《Journal of Forestry Engineering》 2019-01
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Wood defect recognition based on optimized convolution neural network algorithm

LIU Ying;ZHOU Xiaolin;HU Zhongkang;YU Yabin;YANG Yutu;XU Chengyi;College of Mechanical and Electrical Engineering,Nanjing Forestry University;National Engineering Research Center of Biomaterials for Mechanical and Electrical Packaging Products;  
Deep learning technology is a hot spot in machine learning research at present. Establishing and simulating the neural network of human brain to analyze the characteristics of data information were extracted to imitate the working way of human brain,which has shown great advantages in image processing. In this paper,with the help of the optimal sparse representation characteristics of non-sampling shear wave transform and simple linear iterative clustering algorithm,the pixel compactness and the edge contour of the image could be well preserved. Then an optimized convolution neural network algorithm was developed to improve the accuracy rate of wood nondestructive testing to solve defect localization inaccuracy as well as contour and boundary information incompleteness,which further improved the defect characteristics recognition accuracy. Firstly,the non-sampling shear wave transform was used to pretreat the collected wood images,which can reduce the complexity of image processing and the amount of computation while retaining the defect features of the wood images. Secondly,the convolution neural network was used to design a deep algorithm structure for the wood images. At the same time,the simple linear iterative clustering algorithm was also used to improve the initial model,from which the relatively accurate wood defect contour was extracted. Finally,the convolution neural network algorithm was optimized by adjusting the parameters and debugging optimizer repeatedly to improve the learning and computing efficiency,and refine the extraction of the wood defect contour. The optimization improved the processing precision with the reduction of computational complexity,and it had a better robustness. In addition,this algorithm has an excellent recognition effect on wood defects,and the recognition accuracy reached 98.6% while the recognition time was relatively shorter compared with those of the radial basis function(RBF) neural network,back propagation-radial basis function(BP-RBF) hybrid neural network and normal convolution neural network.
【Fund】: 国家林业局“948”项目(2014-4-48);; 江苏省政策引导类计划(国际科技合作)项目(BZ2016028)
【CateGory Index】: TP391.41;TP183
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