An improved method for swing measurement based on monocular vision to the payload of overhead crane

Abstract

In order to solve the problem that the blurred image of a moving object decreases accuracy in the process of detecting the payload swing angle of an overhead crane based on vision, and the tracking failure caused by the drastic change of grey targets, a robust real-time detection method of the load swing angle of a bridge crane is proposed. This method uses a spherical marker attached to the load, which is insensitive to rotation and tilt when it is detected. First, it uses the mean shift algorithm combined with Kalman filter to track the moving objects in the image plane continuously, and then integrates the method of minimum area circle to detect the spherical marker image in the region of interest accurately and quickly. Finally, combined with the geometric method, the real-time swing angle is calculated. In addition, the angle diagram method is used to increase the speed of calculating the swing angle. The experimental results show that the method is effective for detecting the load target swing angle of different trolley motion speed.