# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 5 Conclusions  
We proposed a method for detecting and classifying bearing surface defects. The main content and innovations of this work were as follows:  
1. A local multi-neural network algorithm (Lc-MNN) for image segmentation is proposed; the method includes three stages: wavelet feature extraction, Lc-MNN region division, and Lc-MNN classification. The classification features are obtained by expanding the images at the various layers of the wavelet transform. The Lc-MNN regional division divides the area near the initial segmentation boundary into a region of training samples and a region of samples to be classified, and a polygon fitting algorithm is used to divide the above area into multiple local areas. The Lc-MNN  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{8}{c}{**BP Input Value**} & \multicolumn{8}{c}{**BP Output Value**} & \multirow{2}{*}{
\begin{tabular}{c} **Expected** \\ **Output Value** \\ **Output Value** \\ \end{tabular} } \\  & \multicolumn{1}{c}{**6 (10\({}^{\textbf{1}}\))**} & \multicolumn{1}{c}{**7 (10\({}^{\textbf{1}}\))**} & \multicolumn{1}{c}{**13 (10\({}^{\textbf{1}}\))**} & \multicolumn{1}{c}{**14 (17\({}^{\textbf{2}}\))**} & \multicolumn{1}{c}{**21 (29\({}^{\textbf{2}}\))**} & \multicolumn{1}{c}{**47 (57\({}^{\textbf{2}}\))**} & \multicolumn{1}{c}{**Over**} & \multirow{2}{*}{**Bruise**} & \multirow{2}{*}{**Scratch**} & \multicolumn{1}{c}{**Output Value**} \\  & \multicolumn{1}{c}{**(1 classification process classifies the pixels in each region to be classified using the neural networks within the region to discriminate target pixels and background pixels. After combining the target pixels obtained and performing some post-processing, one obtains the segmentation results of higher precision. The experiments indicated that the proposed algorithm can effectively improve the accuracy of segmentation, which is one of the innovations of the algorithm.
2. We proposed an SCV algorithm for feature selection. The algorithm first removes similar features through correlation analysis, further screens the results using the scalar feature selection algorithm, and finally uses the feature vector selection algorithm to select the final classification features. The experiments indicated that the SCV algorithm can effectively improve the classification accuracy and avoid large-scale computation, which is another innovation of the algorithm developed in this research.  
Through comparative experiments, and with the two improvements described above, we found that the identification rate reached 99.5% and large-scale calculations could be avoided in experiments on 600 test samples with three types of defect. In future work, we hope to add more sample images to the bearing surface defect data and improve the network structure for defect feature extraction. We also plan to study defect detection and classification and identification techniques based on deep learning and the automatic iteration of internet-based classifiers.  
[PERSON] and [PERSON] made substantial contributions to the conception and design. [PERSON] was involved in drafting the manuscript. [PERSON] acquired data and analysis and conducted the interpretation of the data. The critically important intellectual content of this manuscript was revised by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Social Science Fund of China (20 BGL141).  
This study is only based on theoretical basic research. It does not involve human subjects.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.