# Wood Defect Detection Based on Depth Extreme Learning Machine
## 1 Introduction  
Today's wood products are manufactured under increasingly stringent requirements for surface processing. In developed countries such as Sweden and Finland with developed forest resources, the comprehensive use rate of wood is as high as 90%. In sharp contrast, the comprehensive use rate of wood in China is less than 60%, causing a serious waste of resources. With China's rapid economic development, people are increasingly pursuing a high-quality life, which will inevitably lead to an increase in demand for wood and wood products, such as solid wood panels, wood-based panels, paper and cardboard, and other consumption levels are among the highest in the world. The existing wood storage capacity and processing level make it difficult to meet the rapid growth demand. The lack of wood supply and the low use rate have led to the limited development of China's wood industry. Therefore, it is necessary to comprehensively inspect the processing quality of logs and boards to improve the use rate of wood and the quality of wood products.  
The nondestructive testing of wood can accurately and quickly make judgments on the physical properties and growth defects in wood, and nondestructive wood testing and automation can berealized. In recent years, the combined application of computer technology along with detection and control theory has made great progress in the detection of wood defects. In the nondestructive testing of wood surfaces, commonly used traditional methods include laser testing [1; 2], ultrasonic testing [3; 4; 5], acoustic emission technology [6; 7], etc. Computer-aided techniques are a common approach to surface processing, as they are efficient and have a generally high recognition rate [8; 9]. Deep learning was first proposed by [PERSON] in 2006; in 2012, scholars adopted an AlexNet network based on deep learning to achieve computer vision recognition accuracy of up to 84.7%. Deep learning prevents dimensionality in layer initialization and represents a revolutionary development in the field of machine learning [10; 11]. More and more scholars are applying deep learning networks in wood nondestructive testing. [PERSON] [12] et al. used a linear array CCD camera to obtain wood surface images, and proposed a hybrid total convolution neural network (Mix-FCN) for the recognition and location of wood defects; however, the network depth was too deep and required too much calculation. [PERSON] [13] and [PERSON] [14] used the Mask R-CNN algorithm in wood defect recognition, but they used a combination of multiple feature extraction methods, which resulted in a very complex model. However, the current deep learning algorithms still have problems such as inaccurate defect location, incomplete defect contour and boundary information in the wood defect detection process. To solve the above problems and effectively meet the needs of wood processing enterprises for wood testing, we carry out the research of this article.  
The innovations of this article are: (1) Simple pre-processing of wood images using nonsubsampled shear wave transform (NSST), reducing the complexity and computational complexity of image processing, as the input of convolutional neural network; (2) application of a simple linear iterative clustering (SLIC) algorithm to enhance and improve the convolutional neural network to obtain a super pixel image with a more complete boundary contour; (3) use of genetic algorithm to improve extreme learning machine and classified the obtained image features. Through the above method, the accuracy of defect detection is improved, and the recognition time is truncated to establish an innovative machine-vision-based wood testing technique.