# Research on the Application Status of Machine Vision Technology in Furniture Manufacturing Process
## 2 Methods
### Quality Detecting  
The quality detecting during furniture manufacturing process is to ensure the quality of furniture components and parts and prevent the outflow of defective products. For the panel furniture, the objects that need to be examined include dimensional defect, dimensional accuracy of hole processing, surface defect, and color difference. At present, manual detection is still the main method in furniture workshops. Color and surface defects are identified by employees' experience, and the dimension and position of the hole are mostly measured by tape [16]. The detection efficiency is low, the error rate of the check process is high, and there is also a risk of scratching the panel during manual detection [17]. The application of MV inspection can make up for the deficiencies of manual inspection, achieving higher efficiency and lower error rate, and making the measurement data more precise during the non-contact inspection process. In the beginning of quality detecting technology based on MV, three steps of image acquisition, image processing, and image segmentation are needed to make preliminary preparation [18]. (1) Image acquisition  
Image acquisition is an important link in the realization of MV quality inspection technology. Acquiring high-quality images can reduce the difficulty of subsequent image processing. In the field of traditional machine vision, two visible light sensors, either charge-coupled devices (CCD) and complementary metal-oxide-semiconductor sensors (CMOS [19]), are often used. Their photoelectric conversion principle is the same. Compared to CCD, CMOS has the advantages of low power consumption, but it pro-duces more noise. CCD sensors have high sensitivity and low read noise, but consume high power. Infrared photoelectric sensors can capture thermal data and play an important role in the measurement of temperature. Compared with optical sensors, it is more suitable for all-weather work, and it is more effective in detecting long-distance targets. In recent years, sensors that incorporate 2D materials (metals, semi-metals, semiconductors and insulators, etc.) have become strong competitors to traditional sensors due to their thinness  
Figure 3: Flow chart of information collection. and wide detection band. In the future, they may become the main sensors for the next generation of machine vision [20]. Image preprocessing includes image denoising, image enhancement and other operations. Common denoising methods include median filtering, mean filtering, Gaussian filtering, bilateral filtering, etc., which can effectively reduce image noise [21]. The commonly used image enhancement methods are gray transformation, color space change, etc., which can effectively reduce the interference of unimportant information, such as image background on subsequent image processing. In recent years, deep learning has been applied to image noise reduction techniques and performed well in practical applications [22]. [PERSON] et al., [PERSON] et al., and [PERSON] et al., apply convolutional neural network (CNN) to denoise images, and the effect is good in practical denoising applications, which is better than traditional methods [23; 24; 25]. Image segmentation is an important basis for feature extraction. In the actual application process, according to different usage scenarios, selecting an appropriate image segmentation algorithm can effectively improve the quality of the segmented image and be better applied to subsequent operations. Traditional image segmentation methods mainly include threshold-based segmentation methods, edge-based segmentation methods, region-based segmentation methods, and specific theory-based segmentation methods [26]. In recent years, image segmentation based on deep learning has become the mainstream of research.