# End Face Attitude Detection of Special Steel Bars Based on Improved DBSCAN
## 2 Related Work and Related Definition  
### Related Work  
At present, most research on the detection of steel bar end faces focuses on the automatic counting of the steel bar, the spraying of a code, the spraying of paint, and the recognition of characters [33, 34, 35, 36]. [PERSON] [33] mainly employed morphological methods to segment the end faces of different steel bars and counted the number of steel bars. [PERSON] [34] used an iterative training method to train the SVM classifier for many times, and counted steel bars. [PERSON] [19] utilized HT to locate the center of steel bars' end faces, which provided positioning data for the spray code robot. [PERSON] et al. [35] used HT to locate a steel bar's end face and spray paint the steel bar's end face. [PERSON] et al. [36] used HT to segment  
Figure 2: Steel bar end face identification patterns: (**a**) Numerical symbols. (**b**) QR code symbols. (**c**) Label with product information and QR code.  
the image of a steel bar's end face, and identified the bar's end face characters. As can be seen in Table 1a, most of the current methods for detecting and localizing steel bars' end faces only focus on quantity and relative position, but not the attitude of the end face. And most of the current research uses HT to recognize the steel bars' end faces. Despite this, HT circle detection is time-consuming, sensitive to noise, and has low accuracy in complex backgrounds.  
It is important to note that there are many other methods of circle detection besides HT, as shown in Table 1b. [PERSON] et al. [37] proposed an algorithm of curvature-aided HT for circle detection (CACD), and by pre-estimating the curvature, the cumulative operation of all points can be avoided and interruptions between different scales avoided, making circle detection faster and more accurate. However, appropriate edge detection information is required to calculate the curvature. [PERSON] et al. [38] proposed a fast circle detection algorithm using differential region sampling (DRSCD), but it still needs to reach a certain number of points on the candidate circle. The random sampling method is limited by random characteristics, and it is difficult to adapt to many noise points or complex scenes. When there are many false points, it not only affects the detection speed, but also affects the actual accuracy of detection. [PERSON] et al. [39] proposed an improved RCD and used an effective refinement strategy to improve the accuracy of circle detection. Also, due to random sampling, multiple attempts were required to obtain valid sampling points. [PERSON] et al. [40] proposed a new circle detection algorithm based on inscribed triangles (ITCD), which achieved good detection results, but it is difficult to ensure the accuracy of large-diameter circles with occlusions when multiple small arcs are reserved. [PERSON] et al. [41] proposed an evolutionary algorithm to automatically detect circles in images (EACD), which performed well on synthetic and hand-drawn images, but needs to be improved in real images. Therefore, it is necessary to study circle detection methods to solve the problem of low detection accuracy and poor anti-noise ability of the current circle detection methods in complex scenes.