# Steel Roll Eye Pose Detection Based on Binocular Vision and Mask R-CNN
## 6 Posture Detection Test
### Visual Inspection Accuracy Experiment  
The final visual detection error was analyzed by employing the calibration method and detection process detailed in the preceding section. These techniques were applied to conduct roll eye positional attitude detection experiments on steel rolls that have been assigned specific positional attitudes at the coil packaging site, thereby obtaining the magnitude of the detection errors. The sample images captured by the left and right cameras are illustrated in Figure 23. \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Num**} & **Angle of** & **The Center** & **The Ordinate** & **Calculate the** & **Calculate the** & **Calculate the** & **Calculate the** \\  & **Deflection (\({}^{\circ}\))** & **Absissa of** & **of the Center** & **Deflection** & **Abscissa of the** & **Ordinate of the** \\  & & **the Circle** & **of the Circle** & **Angle (\({}^{\circ}\))** & **Circle Center** & **Circle Center** \\ \hline
1 & \(-\)5.00 & 285.00 & 309.00 & \(-\)5.16 & 285.23 & 308.72 \\
2 & \(-\)15.00 & 264.00 & 309.00 & \(-\)15.33 & 264.16 & 308.88 \\
3 & \(-\)30.00 & 263.00 & 307.00 & \(-\)29.82 & 263.49 & 306.53 \\
4 & 30.00 & 272.00 & 376.00 & 30.29 & 272.18 & 376.25 \\
5 & 15.00 & 232.00 & 282.00 & 14.67 & 232.27 & 281.89 \\
6 & 5.00 & 186.00 & 270.00 & 5.18 & 186.05 & 269.77 \\
7 & \(-\)10.00 & 197.00 & 301.00 & \(-\)10.45 & 197.36 & 300.35 \\
8 & \(-\)15.00 & 212.00 & 307.00 & \(-\)15.22 & 212.15 & 306.81 \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental results of elliptic parameter feature extraction. Figure 22: Effect of ellipse fitting. A total of three sets of experiments were conducted, each involving steel coils of different specifications. Within each set, eight distinct positions were designated for position detection experiments. To increase the precision of the experiment, the pitch angle was fixed at 0\({}^{\circ}\). One of these sets was selected to analyze the deviation of each detection, as presented in Table 2. reveals that the maximum deviations for the pitch angle, deflection angle, and centroid coordinates are 2.17\({}^{\circ}\), 2.24\({}^{\circ}\), 3.53 mm, 4.05 mm, and 4.67 mm, respectively. These results satisfy the precision requirements for the automatic docking installation of steel coils. In this paper, Mask R-CNN-based visual inspection technology was employed to successfully achieve image segmentation of the mounting area and feature extraction of the mounting holes, thereby meeting the accuracy requirements for the automatic docking and installation of corner guards. In tasks involving instance segmentation and fine target segmentation, Mask R-CNN demonstrated superior performance, surpassing other deep-learning algorithms such as YOLO, Faster R-CNN, and RetinaNet. Mask R-CNN not only accurately localized targets but also generated high-quality pixel-level masks for each target, enabling precise instance segmentation. This capability confers a unique advantage in target recognition and segmentation tasks within complex scenes. Compared to the YOLO algorithm, while YOLO boasts a significant advantage in processing speed and enables real-time detection, it solely provides bounding box information