## 5 Experiment
### Experimental Verification of Anchor-Rod Positioning  
Figure 14(a) is the result of the improved YOLOv7 model. The samples in the picture are error free, and the confidence is higher than the threshold. At the same time, the size and location information of each detection box are obtained to calculate the pixel coordinates of the center point. Figure 14(b) is the corresponding depth map after alignment. The edge of the depth map is invalid information, and the information of the middle three rows of anchor rods is complete. The detection frames extracted from the RGB image are mapped to the depth map as ROI, and only the depth map in ROI is denoised to complete the depth information and extract the depth value of the center point of the detection frame. Figure 14: Comparison of experimental results. (**a**) YOLOv7. (**b**) CLAHE + YOLOv7. (**c**) CLAHE + CBAM + YOLOv7. (**d**) CLAHE + BiFormer + YOLOv7. (**e**) CLAHE + BiFormer + CBAM + YOLOv7. Figure 15: (**a**) Network detection effect. (**b**) Depth rendering after denoising the positioning experimental verification. Through the coordinate transformation of the pixel coordinates, combined with the depth value, the position coordinates in the RGB camera coordinate system are obtained, and the three rows of anchor rods are fitted with straight lines to get the corrected position coordinates. For example, Figure 16 shows the fitting lines for the three rows of anchor rods. The pixel coordinate value, depth value, coordinate measurement value, conversion coordinate value, and fitting coordinate value of the target point are shown in Table 4.  
Through Figure 15, the target-detection model of the YOLOv7 + CLAHE + BiFormer + CBAM fusion is reliable for anchor-rod recognition in low and uneven illumination environments and can accurately detect targets. According to the data comparison in Table 4, it can be concluded that the depth value of the target point obtained by the depth map combined with the RGB image has a minor error with the actual measured value. The \(Y\)-axis coordinate has no significant deviation from the measured value, but the \(X\)-axis coordinate has a large error in individual cases. By fitting the straight line to correct the coordinates, the more significant \(X\)-axis coordinate deviation is improved. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Row** \\ **Number** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Pixel** \\ **Coordinate** \\ **Value/Pixel** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Coordinate** \\ **Value/m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Coordinate** \\ **Measurement** \\ **Value/m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Conversion** \\ **Coordinate Values/m** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Fitting Coordinate** \\ **Value/m** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Absolute Value** \\ **of an Error/mm** \\ \end{tabular} } \\ \hline \multirow{4}{*}{1} & (384,53) & 1.506 & (\(-\)0.805,1.200,1.500) & (\(-\)0.804,1.192,1.506) & (\(-\)0.804,1.192,1.506) & (1,8,6) \\  & (361,341) & 1.503 & (\(-\)0.805,0.410,1.500) & (\(-\)0.807,0.405,1.503) & (\(-\)0.804,0.405,1.503) & (1,5,3) \\  & (330,680) & 1.496 & (\(-\)0.810,\(-\)0.400,1.500) & (\(-\)0.802,\(-\)0.403,1.496) & (\(-\)0.804,\(-\)0.403,1.496) & (6,3,4) \\  & (690,186) & 1.507 & (0.112,0.800,1.500) & (0.126,0.799,1.507) & (0.114,0.799,1.507) & (2,1,7) \\  & (698,500) & 1.502 & (0.110,0.050,1.500) & (0.103,0.051,1.502) & (0.114,0.051,1.502) & (4,1,2) \\  & (961,57) & 1.508 & (0.836,1.220,1.500) & (0.831,1.225,1.508) & (0.835,1.225,1.508) & (1,5,8) \\
3 & (1003,329) & 1.502 & (0.840,0.450,1.500) & (0.836,0.454,1.502) & (0.835,0.454,1.502) & (5,4,2) \\  & (1055,663) & 1.498 & (0.828,\(-\)0.450,1.500) & (0.839,\(-\)0.448,1.498) & (0.835,\(-\)0.448,1.498) & (7,2,2) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of positioning. Figure 16: Fitting straight-line diagram.