# Extraction and Detection of Surface Defects in Particleboards by Tracking Moving Targets
## 1 Introduction  
As one of the three artificial boards, particleboards can be made of chipped broken wood or wheat stalk, rice straw, bamboo, etc. The basic material used for particleboards is very small and can come from different sources. Particleboards are widely used in many fields due to their advantages of low production cost, high hardness, and wide market availability. Since the 21 st century, energy conservation has become an important indicator of the technical level of industrial production. The problem of poor forest resources and low forest coverage has meant the industrial production of particleboards has become an important method to make up for this situation. At present, in the field of surface defect detection of particleboards, most production lines are technologically backward, with some even relying on artificial, visual inspection. This method is not only inefficient but also costs a lot of manpower, and the results are not exact enough. In recent years, many scholars have made contributions to improve the technology of particleboard production line, including those involving technical fields such as impact resistance and internal bonding strength [1; 2; 3; 4; 5; 6; 7; 8; 9]. In Reference [1], samples of high-density homogeneous particleboards of sugarcane bagasse and castor oil polyurethane resin were manufactured and subjected to low-velocity impacts using an instrumented drop weight impact tower and four different energy levels. Reference [3] evaluated the physical and mechanical properties of particleboards manufactured from a mixture of sycamore leaves and wood particles. In Reference [6], the effects of grit sizes of sand belt, feeding speed, and the feed power of the heads of the sander on surface roughness of the particleboard panels were investigated. Reference [7] evaluated the physical, mechanical, and durable properties of sorghum bagasse particleboards (SBP), which are layered by several materials. Despite these numerous studies, the detection of surface defects has seldom been reported. In the field of automatic detection of surface defectsdefects, some scholars have studied detection methods for the characteristics of steel, glass, and other materials [10; 11; 12; 13; 14; 15; 16; 17; 18; 19], but few have studied the surface defect detection technology on particleboards. In Reference [10], classical convolutional neural networks (CNNs) trained in pure supervised manner was used to detect defects on steel surfaces. The defects were identified by the reflection of light on the steel surface owing to the good reflectance of steel materials. Although this method can effectively identify steel surface defects, it takes a long time to calculate. Moreover, it is not suitable for rough surface materials, such as particleboards, as it cannot detect defects by reflective characteristics. A detection algorithm for the recognition and segmentation of defects in mobile phone screen glass (MPSG) was proposed in Reference [12]. The combination of subtraction and projection (CSP) was used to identify defects on the MPSG image, which could eliminate the influence of fluctuation in ambient illumination. Reference [17] proposed an efficient similarity measure for the detection of surface defects in printed circuit boards (PCB). The method could measure the similarity between the scene image and the reference image of PCB surface without the need to compute image features such as eigenvalues and eigenvectors. The method proposed in Reference [18] was aimed at improving quality control in the ceramic tile industry. An automated inspection system for ceramic tile based on image processing techniques was used to detect edge damages and middle cracks on the surface of the tile. Reference [19] proposed a method for detecting defect on air-bearing surfaces (ABS), which has variance luminance intensity. The co-occurrence matrix was used to avoid the variance intensity of ABS images. However, these methods are only suitable for the detection of small defects.