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Robust and reliable diagnostic methods are desired in various types of industries. This article
presents a novel approach to object detection in industrial or general ultrasound tomography.
The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle,
models use convolution layers to extract common features of signals, which are passed to
dense layers responsible for predicting the number of objects or their locations and sizes.
Predicting the number and properties of objects are characterized by a high value of the
coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution
can result in a reliable and low-cost method of object detection for various industry sectors.
