Electromagnetic systems, in particular microwave/terahertz sensing technologies, are the newest among nondestructive sensing technologies. Currently, increased attention is pointed towards their use in various applications. Among these, food inspection stands out as a primary area due to its potential risk to human safety. As a result, substantial efforts are currently focused on utilizing microwave/terahertz imaging as a tool to enhance the efficacy of food quality assessments. This paper deals with the exploitation of microwave/terahertz imaging technology for food quality control and assessment. In particular, the work aims at reviewing the latest developments regarding the detection of internal quality parameters, such as foreign bodies, i.e., plastic, glass, and wood substances/fragments, as well as checking the completeness of the packaged food under consideration. Emphasis is placed on the (inline) inspection of wrapped/packaged food, such as chocolates, cookies, pastries, cakes, and similar confectionery products, moving along production conveyor belts. Moreover, the paper gives a recent overview of system prototypes and industrial products and highlights emerging research topics and future application directions in this area.

The importance of radar-based human activity recognition has increased significantly over the last two decades in safety and smart surveillance applications due to its superiority in vision-based sensing in the presence of poor environmental conditions like low illumination, increased radiative heat, occlusion, and fog. Increased public sensitivity to privacy protection and the progress of cost-effective manufacturing have led to higher acceptance and distribution of this technology. Deep learning approaches have proven that manual feature extraction that relies heavily on process knowledge can be avoided due to its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical-based approach, where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances, taking the recent progress in both categories into account. For every category, state-of-the-art methods are introduced, briefly explained, and their related works summarized. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking dataset to provide a common basis for the assessment of the techniques’ degrees of suitability.

The importance of radar-based human activity recognition increased significantly over the last two decades in safety and smart surveillance applications due to its superiority towards vision-based sensing in the presence of poor environmental conditions, like illumination, increased radiative heat, occlusion, and fog. An increased public sensitivity for privacy protection, and the progress of cost-effective manufacturing, led to a higher acceptance and distribution. Deep learning approaches proved that the manual feature extraction that relies heavily upon process knowledge can be avoided by its hierarchical, non-descriptive nature. On the other hand, ML techniques based on manual feature extraction provide a robust, yet empirical based approach where the computational effort is comparatively low. This review outlines the basics of classical ML- and DL-based human activity recognition and its advances while taking recent progress of both categories into regard. For every category, state-of-the-art methods are introduced, briefly explained and discussed in terms of their pros, cons and gaps. A comparative study is performed to evaluate the performance and computational effort based on a benchmarking data set to provide a common basis for the assessment of the techniques’ degree of suitability.

Recent studies have shown that, alongside microwave-based and short-wave infrared (SWIR) technologies, terahertz-based sensing is a promising and reliable method for non-destructive testing (NDT) of food in order to detect foreign objects. This improves the safety and quality of low-hydration processed or natural foods as it is expanding the sensing capabilities in the electromagnetic spectrum by revealing further insights thanks to the higher resolution and penetration depth. Despite this broad coverage, limitations and drawbacks still remain in terms of accuracy and performance under industrial conditions and costs, particularly in the terahertz region where penetration depth decreases significantly. In order to address this crucial point, we have developed an NDT system based on a commercial-off-the-shelf product for terahertz imaging and investigated its capabilities primarily with regard to the detection of low-density foreign objects (LDFO) of varying sizes and materials. The evaluation is based on selected image quality measurement indices, e.g. Structural Similarity Index (SSIM) and variants, Feature Similarity Index (FSIM), Chi-squared distance, Mean Opinion Score (MOS), which were applied to images of different product types, foreign objects, and levels of attenuation. To this end, we introduce and make publicly available a novel dataset of industrial confectioneries ranging from wafers to chocolate-covered cookies. In addition, our presented framework offers a range of functions for comprehensive quality assessment, including the measurement process, a streamlined approach for postprocessing sub-terahertz data, and evaluation methods. According to results of threshold-based binary classification a total accuracy of 84 % was achieved.