Crane Hook Detection Based on Mask R-CNN in Steel-making Plant

Abstract

This paper proposes a solution based on machine vision and deep learning for the hidden safety problems that the hook does not match the ladle trunnion correctly, which is one of the hidden dangers during the crane lifting the ladle. Mask Region Convolutional Neural Network (Mask-RCNN) was introduced to segment the crane hook and find the bottom point of the hook contour. The trunnion center point can be directly located in image by painting a special color on it. Then determine whether the hook and trunnion match correctly by calculating the angle between the horizontal line and the line connecting the bottom point of the hook and the trunnion center point. According to the experimental rsulrs of 100 test images, that the average accuracy (AP) of our methods for hook segmentation can reach 92%. And the accuary of the safety judgment algorithm has reached 96%.