A Machine Vision Algorithm Approach for Angle Detection in Industrial Applications

In automatic feeding systems, feeding of characteristic workpieces by mechanical tools causes accuracy and cost difficulties. For this reason, in systems where special workpieces are fed, image processing applications are necessary to obtain characteristic features of a product. In this study, a novel image processing algorithm is proposed for feeding a workpiece which has characteristic geometrical structures. The proposed algorithm is based on obtaining geometrical and rotational properties of the product and the gradient-based analysis as follows. The first step is to extract features from the shape of the workpiece, this step includes noise reduction, filtering, and edge detection operations. The gradient values of the edge information are used to create the angle-length vector pair in the second step. The workpiece rotation information is derived from length values indexed with angle information. The last step involves determination of the workpiece position in the 2D coordinate system. The coordinate information is used to determine the position of the gripper holder. The coordinates and angle are transmitted to the feed control. The proposed algorithm is applied on the 800 images that are collected from manufactured products. Rotation angle of the workpiece is determined by a tolerance of 1.5°. It is seen that results have sufficient accuracy for industrial applications.