## 4 Feature Extraction
### Anchoring Hole Feature Extraction  
To accurately extract the edge features of the steel belt and the center coordinates of the anchor holes, it is essential to analyze the image characteristics of the steel belt first in order to develop a suitable extraction method. As shown in Figure 5(a), the anchor holes are uniformly distributed along the central axis of the steel belt's length and exhibit circular or elliptical edge features. Therefore, the following approach can be adopted to extract the center coordinates of the steel belt anchor holes: Firstly, perform binary thresholding on the original image to highlight the contour features of the steel belt region and apply median filtering to remove salt-and-pepper noise, enhancing the image quality as depicted in Figure 5(b). Next, conduct edge detection on the binary image to obtain the contour information of the steel belt image, as shown in Figure 5(c). Then, fit straight lines to the edge information of the steel belt to determine the slopes \(k_{1}\) and \(k_{2}\) of the fitted lines for the upper and lower edges of the steel belt, respectively, and define the ROI (region of interest) of the steel belt, as indicated by the red region shown in Figure 5(d). Finally, perform ellipse fitting within the ROI of the steel belt and use the center point of the fitted ellipse as the center of the steel belt anchor holes in the image, as illustrated by the green ellipse and yellow center point shown in Figure 5(d).  
The anchor-hole-positioning method based on monocular vision applied in the coal mine roadway anchor support process primarily utilizes a camera to capture the laser spot information generated by a laser pointer on the coal wall. This method constructs a visual positioning model for anchor holes on steel belts, enabling the detection of anchor hole poses. Environmental variations during this operation mainly include changes in dust and brightness.  
Since the positioning device described in this paper is mounted on the anchor-drilling robot, which completes both tunnel excavation and roof support processes sequentially, the concentration of dust in the anchor support process does not fluctuate significantly. Thus, there is no situation where high dust concentration prevents the laser beam from penetrating dust to reach the coal wall and form a spot. Consequently, changes in dust concentration do not adversely affect the accuracy and reliability of the visual positioning method.  
Coal mine roadways are enclosed spaces with generally low brightness, allowing laser beams emitted by the laser pointer to display spots on the coal wall. However, in conditions of high brightness, a monocular camera may fail to capture spot information, thereby rendering the positioning method described in this paper ineffective. Therefore, the dim environment of coal mine roadways does not adversely affect the accuracy and reliability of the visual positioning method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Items** & \multicolumn{3}{c}{**Ellipse Fitting Method**} & \multicolumn{3}{c}{**Gaussian Fitting Method**} & \multicolumn{3}{c}{**Proposed Method**} \\ \cline{3-8}  & & **X-Direction** & **Y-Direction** & **X-Direction** & **Y-Direction** & **X-Direction** & **Y-Direction** \\ \hline \multirow{3}{*}{Spot1} & Mean & 18.87 & 20.29 & 19.07 & 20.56 & 19.05 & 20.55 \\  & Standard Deviation & 1.19 & 1.37 & 0.67 & 0.71 & 0.56 & 0.62 \\  & Mean & 24.08 & 20.11 & 24.29 & 19.87 & 24.23 & 19.56 \\  & Standard Deviation & 1.12 & 1.01 & 0.53 & 0.51 & 0.46 & 0.37 \\  & Mean & 22.32 & 22.07 & 21.86 & 21.89 & 21.64 & 21.68 \\  & Standard Deviation & 0.85 & 0.72 & 0.36 & 0.31 & 0.35 & 0.32 \\  & Mean & 21.76 & 20.82 & 21.74 & 20.77 & 21.64 & 20.60 \\  & Standard Deviation & 1.05 & 1.03 & 0.52 & 0.51 & 0.46 & 0.44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of mean and standard deviation of three methods for spot center extraction results.