Learning-Based See-Through Sensing Suitable for Factory Automation

A scanner for image reconstruction of a structure of a target object uses a neural network trained to classify each segment of a sequence of segments of a modified wave into one or multiple classes. The sequence of segments corresponds to the sequence of layers of the target object, such that a segment of modified wave corresponds to a layer having the same index in the sequence of layers as an index of the segment in the sequence of segments. The scanner executes the neural network for each wave modified by penetration through the layers of the target object to produce the classes of segments of the modified waves. Next, the scanner selects the classes of segments of different modified waves corresponding to the same layer to produce an image of the layer of the target object with pixel values being functions of labels of the selected classes.