Object recognition by neural network using thickness data from acoustic image

Methods of inputting stereoscopic information to neural networks and of imaging by ultrasonic sensors were examined from the perspective of three-dimensional object recognition, and a new recognition method was developed for depth images in neural networks. Because a perceptron neural network is used as the recognition stage and ultrasonic sensors arranged in an array for both reception and transmission are used as the shape measurement stage, this method performs compression of the stereoscopic information of an object into a frame of depth information by measuring the planar distribution of the distances to the object's surface (using a pulse echo method); it has the advantage of being able to efficiently recognize an object freely moving in any direction (forward and backward, right and left, and up and down) and having any orientation within a plane, by using depth information. Since a neural network is used, even when different features are extracted from the object, direct recognition is possible. In this paper, which addresses the issue of three-dimensional object recognition using perceptrons, we describe a measurement and recognition system capable of preprocessing stereoscopic information that can be freely positioned in three dimensions to allow recognition by perceptrons, and we present the results of recognition tests. Finally, we propose a recognition algorithm that actively uses depth (which is not a feature in two dimensions), show that the algorithm can recognize both the position and orientation of the object, and demonstrate its practicality.