# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 2 Related Work
### Defect Image Segmentation  
The accurate extraction of defects on the bearing surface relies mainly on image segmentation. The image segmentation method can be divided into edge segmentation, threshold segmentation, regional growth segmentation, and image segmentation based on a specific theory [14; 15; 16; 17]. In edge segmentation, the edges are detected with the aid of first-order or second-order derivatives and then fitted to form a contour of the region before the image is segmented [18; 19; 20]. In threshold segmentation, an appropriate set of thresholds is obtained so that pixels of the image can be compared with the thresholds to group pixels with similar features in one category. The key is to choose a suitable set of thresholds based on the characteristics of the image [21; 22; 23]. Regional growth segmentation is a serial segmentation algorithm. We find one or more seed pixels, design an index for measuring the similarity between other pixels and the seed pixels, and then, based on this quantitative metric, determine whether to include a peripheral pixel into the area where the seed pixels are located. This iteration continues until there are no matching pixels. The result is the segmented region of the regional growth segmentation algorithm [24; 25; 26].  
Segmentation methods based on specific theories include watershed segmentation methods based on mathematical morphology and segmentation methods based on fuzzy theory, neural networks, graph theory, and models. This type of algorithm [27; 28; 29; 30] involves complex calculations, and the segmentation effect varies from image to image. For online inspection of surface defects on bearings, it is difficult to process images in real-time. By combining the local binary fitting (LBF), energy function, and the modified Lapalcian-of-Gaussian (MLoG) approach, [PERSON] et al. [31] proposed an active contour model to reduce the sensitivity of the initial contour. Similarly, [PERSON] et al. [32] made a trade-off between the accuracy and efficiency of image segmentation using a new adaptive method and a new mutation strategy. [PERSON] et al. [33] proposed a group selection method for multi-threshold image segmentation. By selecting an appropriate number of group algorithms from 11 algorithms, they provided expert systems with problem-solving tools. [PERSON] et al. [34], by introducing a priori information into the model parameters and taking into account the uncertainty of model parameters, solved problems related to accurate data classification during image segmentation, such as the effective estimation of model parameters and the selection of optimal model complexity. [PERSON] et al. [35] combined the adaptive threshold and morphological filtering to achieve a semi-automatic root image segmentation with an average dice coefficient of 0.82 and a Pearson coefficient of 0.8.  
Although some researchers have paid attention to the accuracy of defect segmentation and the problem of defect feature extraction and classification, during the inspection of bearing surface defects the defect area needs to be accurately segmented in real-time, and randomness and complexity coexist in collected images. As a result, a literature search has thus far failed to identify a general segmentation method with both high accuracy and efficiency. Given this, our study applies to the accurate segmentation of defect regions in bearing surface defect images and has moderate complexity.