Object Recognition Using Deep Learning Algorithms and Ultrasonic Signals

In line with the needs of life, new solution methods developed against the object recognition problem are gaining importance. In this paper, a literature search was conducted on object recognition studies using ultrasonic signals. It is aimed to contribute to the literature by proposing an integrated method that can perform object recognition by passing ultrasonic signal data obtained from different objects through pre-processing, feature extraction and classification processes. As a result of the study, a pre-processing technique was applied to extract the signal envelope by editing the data set obtained by making measurements from objects of different diameters and shapes at a certain angle and distance. The data obtained as a result of preprocessing was passed through feature extraction methods known as waveform shape descriptors. Comparisons were made with the most appropriate hyperparameters of deep learning algorithms to achieve high-performance classification rates and comprehensive object recognition with the developed system. Consequently, high performance classification was achieved with MLP and CNN deep learning models used in the classification stage.