Machine-Learning-Based Microwave Sensing: A Case Study for the Food Industry

Despite the meticulous attention of food industries to prevent hazards in packaged goods, some contaminants may still elude the controls. Indeed, standard methods, like X-rays, metal detectors and near-infrared imaging, cannot detect low-density materials. Microwave sensing is an alternative method that, combined with machine learning classifiers, can tackle these deficiencies. In this paper we present a design methodology applied to a case study in the food sector. Specifically, we offer a complete flow from microwave dataset acquisition to deployment of the classifiers on real-time hardware and we show the effectiveness of this method in terms of detection accuracy. In the case study, we apply the machine-learning based microwave sensing approach to the case of food jars flowing at high speed on a conveyor belt. First, we collected a dataset from hazelnut-cocoa spread jars which were uncontaminated or contaminated with various intrusions, including low-density plastics. Then, we performed a design space exploration to choose the best MLPs as binary classifiers, which resulted to be exceptionally accurate. Finally, we selected the two most light-weight models for implementation on both an ARM-based CPU and an FPGA SoC, to cover a wide range of possible latency requirements, from loose to strict, to detect contaminants in real-time. The proposed design flow facilitates the design of the FPGA accelerator that might be required to meet the timing requirements by using a high-level approach, which might be suited for the microwave domain experts without specific digital hardware skills.