# Research on the Application Status of Machine Vision Technology in Furniture Manufacturing Process
## 2 Methods
### Quality Detecting
#### 3.2.3. Surface Defect Identification  
Surface defect identification by MV includes three steps of image pre-processing, defect area localization, and defect feature extraction [41]. Firstly, image pre-processing includes eliminating noise and other interference information, enhancing image contrast, and making the features in the image salient. Secondly, different methods of the model comparison method and image segmentation method, will be used to locate the defect areas for the different objects. Thirdly, some relevant algorithms would be selected to analyze the location area, extract defect features, and identify and classify defect types. At present, the surface defect identification methods based on MV are mainly divided into three categories: defect detection based on image processing, defect detection based on machine learning, defect detection based on deep learning.  
Surface defect detection, which based on image processing, applies an image segmentation algorithm to extract defect surface features. Canny and Sobel operators are the more commonly used algorithms to extract defect edges. And the feature extraction or template matching is applied to identify defects [42]. This defect recognition method is simple, widely used, and has good robustness. But it is difficult to deal with some complex changes in the defect and has no autonomous learning ability. It is only suitable for a single fixed detection target. In recent years, the convolution neural network (CNN), deep belief network (DBN), and residual neural network model (ResNet) that have deep learning capabilities are gradually applied for defect detection [43; 44; 45]. Compared with image processing and machine learning, these deep learning methods have higher detection efficiency and the ability to learn. However, their calculation is complicated, many datasets  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & & **Advantages** & & **Disadvantages** \\ \hline \multirow{4}{*}{Sobel} & \(\bullet\) & Simple & \(\bullet\) & Poor accuracy \\  & \(\bullet\) & Directional & \(\bullet\) & Easy to miss edge \\  & \(\bullet\) & Anti-noise & \(\bullet\) & Wide detection edge \\  & \(\bullet\) & Simple & \(\bullet\) & Easy to miss edge \\ \multirow{2}{*}{Roberts} & \(\bullet\) & Fast & \(\bullet\) & Wide detection edge \\  & \(\bullet\) & High precision & \(\bullet\) & Many limitations \\ \multirow{2}{*}{Prewitt} & \(\bullet\) & Anti-noise & \(\bullet\) & Poor accuracy \\  & \(\bullet\) & Fast & \(\bullet\) & Blurred edges \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._are needed to be trained [46; 47; 48]. [PERSON] et al., (2017) utilized gray level co-occurrence matrix and laws texture energy measures as texture feature extractors, and chose feed-forward back-propagation neural network as classifier to identify wood defects. The results showed that that method had an accuracy of 84.3% [49]. In Table 3, the commonly used defect recognition algorithms in recent years were summarized and the advantages and disadvantages of these different algorithms were analyzed.