# End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN
## 4 Result Analysis of Steel Bar End Face Attitudes
### Analysis of Steel Bar End Face Attitude Detection Results  
The experiment device captured more than 1800 images of the steel bar's end face. The attitude angle value of each image is calculated using the method in this paper. At the same time, the commonly used Hough circle detection (HT) method is employed to extract the circumscribed circle parameters, and the attitude angle value of each image is also calculated. Figure 9a is a comparison curve between the attitude angle value calculated using our method or HT method, and the ideal data. The yellow line represents the ideal curve with an equal interval of 0.2 degrees, the green line represents the attitude angle curve calculated using the HT method, and the red line represents the attitude angle curve  
Figure 8: The platform of detecting and grinding surface defects. calculated using our method. Due to the ideal curve covering the other curves, the yellow line and green line can be seen more clearly on attitude angle curves, whereas the red line is only visible in a few places, which indicates that the calculation results for this paper are closer to the ideal value than for the HT method. For further quantitative evaluation of the accuracy of the two methods, the difference between attitude angle calculated using our method and the ideal value was compared to the difference between attitude angle calculated using the HT method and the ideal value. The results are shown in Figure 9b,c. From Figure 9b, we can see that the error range between the attitude angle value calculated using our method and the ideal attitude angle value is within 0.1 degrees. But in Figure 9c, the error range between the attitude angle value calculated using the HT method and the ideal angle value is within 1.1 degrees. According to the distribution of error data, the distribution of the error data of the method in this paper is relatively uniform. In contrast, the distribution of the error data of the HT method is messy. In addition, the error data of the HT method are not centered at 0, the first half is biased towards data below zero, while the second half is skewed towards data in excess of zero, and there is no specific rule to follow. As can be seen from the results in this paper, the method presented in this paper performs significantly better than the HT detection method. Figure 9: _Cont._  
Researchers have studied the detection of steel bars' end faces in recent years. [PERSON] [33] and [PERSON] [34] counted the steel bars. The method focuses on dividing steel bars' end faces to achieve the purpose of statistical counting. [PERSON] [36] and [PERSON] [35] used the HT method to locate the steel bars' end faces to identify the end face characters and spray paint. Effective positioning results are given in the literature. [PERSON] [19] used HT to locate steel bars' end faces to realize automatic inkjet coding through positioning. The accuracy of the distance between two adjacent bars was analyzed. It can be seen that these studies do not pay attention to the attitude angle of the steel bars' end faces. And we could not find a current study of calculating steel bar end face attitude. However, steel bar end face attitude detection is a key step in surface defect circumferential positioning. In order to illustrate the accuracy of the algorithm, the HT method is used to calculate the steel bar end face attitude, and the method proposed in this paper is compared. The comparison results are presented in Table 2. Using our method, the attitude error value of 1220 images is within an error range of \(\pm\)0.05, accounting for about 67.78% of the total amount of data, while using the HT method, only 174 images are within the error range of \(\pm\)0.05, accounting for 9.67% of the total amount of data. All the error of attitude data in our method are distributed in the range of \(\pm\)0.1; at this time, only 18.89% of the attitude data of the HT method are within this error range.