# Vision-Based Surface Inspection System for Bearing Rollers Using Convolutional Neural Networks
## 1 Introduction  
Bearings are commonly used mechanical components. A bearing's main function is to support mechanical rotation and reduce the friction coefficient during its movement. Since the roller is the most important part of the bearing, its surface quality has a significant impact on the performance and even the life of the bearing, thus, the surface quality of the roller must be extremely high. Bearing rollers are shown in Figure 1 below. Figure 1: Bearing rollers. Rollers are the main pressure-bearing part in rolling bearings and are easily damaged due to defects and other factors. If there are defects on the roller surface, the stability of the bearing will be heavily reduced during use. Therefore, in mechanical design, the geometric accuracy and surface roughness of the roller are typically one level higher than that of the ferrules and raceways. Among the rolling bearings, deep groove ball bearings are mainly used in small- and medium-sized equipment, while roller bearings are widely used in medium- and large-sized machines. They are widely used in passenger transportation, aerospace, and other transportation fields, as well as agricultural machinery, industrial machinery, medical equipment, and other related machinery industries. The bearing roller is the main research object of this paper. Defects inevitably occur on the surface of bearing rollers in the production process. The defects are mainly distributed on the cylindrical surface, chamfers, and end surfaces. Common defect categories include: Damage and scratches caused by mechanical collision; corrosion caused by mechanical aging; and material lacking, at the chamfer, and grind lacking caused in the production process. As can be seen from Figure 2 below. The main defect categories are as follows:  
1. Scratch, as shown in Figure 2a,b. A defect caused by a roller being scratched by other hard objects. 2. Damage, as shown in Figure 2c-f. We describe defects with large areas and irregular shapes as damage. 3. Corrosion, as shown in Figure 2g,h. The defects caused by corrosion. 4. Material lacking at the chamfer, as shown in Figure 2i,j. The roller is sunken at the chamfer, making the contour not a circle. Figure 2: Common defects on the bearing roller: (**a**,**b**) Scratch; (**c**–**f**) damage; (**g**,**h**) corrosion; (i,j) material lacking at the chamfer; (**k**) grind lacking; and (**l**) stamp lacking. Grind lacking, as shown in Figure 2k. The defects caused by insufficient grinding. 6. Stamp lacking, as shown in Figure 2l. The defects caused by insufficient stamping. Figure 2a,c,d,g,i,k,l are images of the end surfaces of the roller, and the rest are images of the cylindrical surface of the roller. These defects have a great influence on the performance and stability of the bearing and must be detected. Visual inspection is a good solution because it can reduce a lot of manual detection. At present, visual inspection technology has been used in many scenarios, such as chip pin and circuit solder inspection [1], workpiece vision measurement [2], plastic bottle defect detection [3], metal product surface defect detection [4; 5; 6], equipment parts identification and classification [7], gear and bearing surface inspection and measurement [8], bearing defect inspection [9], optical character recognition [10], and agricultural product identification [11]. Despite being used in large numbers, there are still many problems with visual inspection in the application of roller surface inspection. In the actual production process, it still relies more on manual inspection, and the inspection efficiency and level are relatively low. Traditional methods used in manufacturing, such as edge detection [12; 13], segmentation [14], and line detection [15; 16], can hardly extract the internal structures and accurately classify each defect category.