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Precise object detection using local feature for robot manipulator

It is difficult to apply object recognition technology to manufacturing industry because the intrinsic characteristics of an object is easily influenced by the surrounding environment such as lighting condition, background complexity and object shape. This paper proposes a precise object detection method for assembling components by more stable feature extraction. To accomplish it, two images are captured by fine-tuning exposure time for the purpose of complementation of features. Adaptive binarization and differential of Gaussian methods are applied to extracting edge information from each image. In the next step, primary features such as contour lines are extracted from the object using the Fast Hough Transform and candidate lines are selected to become geometric conditions of the object. The precise object region is detected by shape analysis using the four vertices computed by the candidate lines. In addition, the internal features of the object are employed to increase the precision of object detection. As a result, the proposed method improved the accuracy of object detection so that it can be useful in the visual servoing using the robot manipulator.