Foreign Object Detection in Aqueous Food Media using Surface Electric Potential and Machine Learning Techniques

Foreign object contamination remains a problem in food processing despite the existing countermeasures to detect and remove unintended objects during the food production process. Most imaging techniques only work well in detecting specific objects but may not be suitable in more challenging problem settings, such as objects with different shapes, con-ductivities, and densities in aqueous food media. This paper introduces a new approach for detecting objects embedded in aqueous food media using surface potential measurements obtained from a three-dimensional cylindrical sensing pipe and convolutional neural network (CNN) classifiers. The simulated electric potential measurements from the surface of the sensing pipe are constructed as two-dimensional images, providing essential information for the CNN classifier to infer the presence of objects and to distinguish opposing conductivities based on distinctive spatial characteristics. Experimental results evaluated on unseen object shapes and orientations suggest that surface potential measurements with low noise interference and CNN classifiers can be used to reliably detect the presence of objects and classify the conductivity of the objects.