# Steel Roll Eye Pose Detection Based on Binocular Vision and Mask R-CNN
## 7 Conclusions  
This paper presents a novel scheme for the precise measurement of the positional parameters of the roll eye of a steel coil using visual detection technology. Initially, the transformation relationships between four coordinate systems were derived based on the camera imaging principle. This enabled the determination of the internal and external parameter matrices, which were then rotated to achieve camera calibration, ensuring high-precision image acquisition. Subsequently, image segmentation was performed using a Mask R-CNN network, complemented by spatial vector segmentation in the RGB color space. This approach accurately extracted the steel coil roll-eye region from complex scenes, effectively addressing the challenge of target region recognition amidst intricate backgrounds. Furthermore, the ellipse parameter extraction technique was integrated with the least-squares method to construct a chi-square equation from the elliptic curve equation. This facilitated the determination of the ellipse's inclination and center position within the image coordinate system. By combining the camera imaging principle with the linear imaging models of the left and right cameras, the scheme successfully resolved the issue of accurately measuring the positional attitude parameters of the steel coil roll eyes.  
The experiments demonstrated that the system design effectively addressed the technical challenges in image processing related to the detection of the positional attitude of steel coil roll eyes. The mask image obtained through the Mask R-CNN-based image segmentation technique exhibited high fidelity, resulting in minimal errors in both ellipse parameter solving and overall visual detection. Specifically, the error in ellipse parameter extraction in the u-direction of the coordinate system was 0.49, while in the v-direction, it was 0.47. The maximum error in the deflection angle was 0.45\({}^{\circ}\). In the actual steel coil roll eye positional pose detection experiments, the maximum deviations for the pitch angle, deflection angle, and center coordinates were 2.17\({}^{\circ}\), 2.24\({}^{\circ}\), 3.53 mm, 4.05 mm, and 4.67 mm, respectively, meeting the accuracy requirements for automatic docking installation. In summary, the proposed machine vision-based steel coil eye position detection method significantly enhances the precision of steel coil eye position detection and provides robust technical support for the automated installation process of steel coils.  
Conceptualization, X.S., J.W. and D.Z.; investigation, X.S., J.W. and D.Z.; writing--original draft preparation, J.W. and Y.W.; writing--review and editing, J.W., Y.W. and X.S.; supervision, X.S.; project administration, X.S. and D.Z.; funding acquisition, X.S. and D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Innovative Group Program of Natural Science Foundation of Hubei Province (grant number: 2023 AFA037) and Major Science and Technology Special Project of Hubei Province (grant number: 2024 BAA014).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.