Steel Roll Eye Pose Detection Based on Binocular Vision and Mask R-CNN

To achieve automation at the inner corner guard installation station in a steel coil packaging production line and enable automatic docking and installation of the inner corner guard after eye position detection, this paper proposes a binocular vision method based on deep learning for eye position detection of steel coil rolls. The core of the method involves using the Mask R-CNN algorithm within a deep-learning framework to identify the target region and obtain a mask image of the steel coil end face. Subsequently, the binarized image of the steel coil end face was processed using the RGB vector space image segmentation method. The target feature pixel points were then extracted using Sobel edges, and the parameters were fitted by the least-squares method to obtain the deflection angle and the horizontal and vertical coordinates of the center point in the image coordinate system. Through the ellipse parameter extraction experiment, the maximum deviations in the pixel coordinate system for the center point in the u and v directions were 0.49 and 0.47, respectively. The maximum error in the deflection angle was 0.45 degrees. In the steel coil roll eye position detection experiments, the maximum deviations for the pitch angle, deflection angle, and centroid coordinates were 2.17 degrees, 2.24 degrees, 3.53 mm, 4.05 mm, and 4.67 mm, respectively, all of which met the actual installation requirements. The proposed method demonstrates strong operability in practical applications, and the steel coil end face position solving approach significantly enhances work efficiency, reduces labor costs, and ensures adequate detection accuracy.