# Detection and Classification of Bearing Surface Defects Based on Machine Vision
## 1 Introduction
### Current Status and Bottlenecks in the Detection and Classification of Bearing Surface Defects  
The bearing surface defect detection system based on machine vision is a complex system that touches upon many fields, including mechanical design, automatic control, computer applications, image acquisition, image processing, image analysis, image interpretation, and pattern recognition. At present, the mainstream visual inspection process for bearing surface defects generally consists of image preprocessing, region-of-interest (ROI) extraction, and pattern recognition. With the continuous development of visual inspection technology for bearing surface defects, research in this area has been successful. For example, [PERSON] et al. [3] designed a new signal-processing algorithm and used acoustic emission technology to measure the size of bearing surface defects. [PERSON] et al. [4] used autoregressive models and envelope analysis to enhance the features when a rolling object enters and exits a defect region and used the duration between two flawed events to estimate the size of a defect. [PERSON] et al. [5] generated training data using information obtained from a high-resolution simulation of rolling bearing dynamics and applied the data to train machine learning algorithms for bearing defect detection. [PERSON] et al. [6] developed a set of metal surface visual inspection experimental equipment based on genetic algorithms and image morphology. Furthermore, [PERSON] et al. [7] proposed three image acquisition schemes suitable for metal surface defect detection. [PERSON] et al. [8] studied the pitting that often occurs on rolling bearings, and chose the area of the defect region, elongation, thickness, roundness, and smoothness of the edge as classification characteristics and as inputs to a neural network to finally identify the type of defect. [PERSON] et al. [9] investigated exterior defects generated in service, such as scratches, and focused on the analysis of geometric characteristics, the shape moment characteristics of binary images of surface defects, and the selection of neural network parameters in the classification of surface defects. [PERSON] et al. [10] studied manufacturing defects such as striping on steel ball bearings, took integrated entropy as the criterion for whether defects exist on a steel ball bearing, and designed a linear classifier to classify defect types based on defect area, shape factor, aspect ratio, roundness similarity, rectangular similarity, and direction angle. [PERSON], [PERSON] et al. [11] used neural network classifier based on single classification to process the data of tool image, proposed a method to determine the tool wear rate based on image analysis, discussed the evaluation of errors, and implemented a special neural wear software in visual basic to analyze the wear position of cutting edge. This work creates a new way for us to use neural network to detect the surface defects of bearings. [PERSON] et al. [12] conducted a visual inspection study on the surface defects of the anti-friction coating on a sliding bearing working surface and classified the defects using 12-dimensional features including 5-dimensional geometry features, 3-dimensional shape features, and 4-dimensional texture features. [PERSON], [PERSON], et al. [13] proposed a two-step method for automatic tool life prediction in the turning process. The development of image-recognition software and an artificial neural network model can be used as a useful industrial tool for low-cost tool life estimation in a turning operation. This conclusion makes us more confident to carry out our work. To summarize, the image segmentation methods used in existing bearing surface defect detection systems are mostly based on traditional methods of threshold segmentation and edge detection or the improved versions of the algorithms. Such algorithms, in their pursuit of inspection speed, do not have high precision in extracting the defect region.