Performing Department
(N/A)
Non Technical Summary
Foreign object contamination in foods post a great threat to the safety of consumers and may cause enormous loss to food manufactures due to lawsuits and recalls. These foreign objects may be endogenous to the food, such as pits and bone particles, or exogenous, such as glass, metal, wood, plastic, and stone. According to worldwide data from Horizonscan, there had been one food recall per week that was related to foreign objects found in food in 2018. Currently, food manufacturers usually rely on two types of technologies, i.e., metal detection and/or X-ray inspection, for foreign object detection in food production as their HACCPs. A metal detector can find metals, including ferrous, nonferrous, and stainless steel, which may be splinters from machinery or fractions of broken metal fragments. But metal detectors do not respond to other non-metal foreign objects that may contaminate foods and the metal detector operation requires a metal free zone on the production line. On the other hand, an X-ray detector can be used for a wider range of foreign objects, including metal, stone, bone, and hard plastics. However, X-ray equipment is expensive, regulated, unsafe for workers, and inefficient to detect light and thin bones, and other light foreign materials, such as cherry stones and insects [1]. Furthermore, each food product may present special technological challenges, since the physical properties of food and packages may respond differently to available detection systems.We propose microwave imaging technology (MIT) which offers non-invasive, non-ionizing, hygienic, and contactless sensing methods for packaged food. Due to the low radiation power density, it is also safer to human body than X-ray. Furthermore, compared with the commonly used optical sensing technology, it provides better detection performance due to higher signal penetration through food, resilience to smoke/gas/moisture/glare/liquid, and no need of light. Moreover, thanks to the recent advancement of semiconductor technology, a variety of microwave chipsets has recently been available and revealed excellent sensing performance. In addition, the size and cost of microwave components have dramatically decreased for rapid prototyping and mass production. However, these state-of-the-art microwave chipsets have yet to be utilized in food inspection (e.g., to automate packaged food-processing) which demands higher efficiency in quality assessment and contamination detection. Our research team will measure electromagentic wave propagation through packaged food and construct the corresponding images to detect foreign objects in packaged food. Machine learning algorithms will be developed and used to precisely identify shape, size, material (metal, glass, plastic, bone, etc), and location of the foreign objects inside the packaged food. The proposed microwave imaging system will broadly benefit food-packaging and food-processing facilities.
Animal Health Component
25%
Research Effort Categories
Basic
50%
Applied
25%
Developmental
25%
Goals / Objectives
To fill the research gap and advance food production automation, our goal is to develop non-invasive, non-ionizing, hygienic, contactless, and intelligent in-line food inspection systems empowered by MIMO (Multiple Input Multiple Output) microwave imaging coupled with machine learning algorithm. Specifically, in this proposed project, we propose to design such systems for the detection of foreign objects in packaged food. Specific objectives areObjective 1: Develop a microwave dual-polarized MIMO-based sensing system and machine learning algorithm using simulation models.Objective 2: Assess the performance of the microwave sensing system using real packaged food.The proposed microwave imaging system will include an ultra-wideband (UWB) antenna array in a circular arrangement fashion around an inline packaging facility. The unique features are to support UWB and dual polarization microwave propagation to the packaged food to identify foreign objects that can cause hazard to human. In addition, radar-based image construction with MIMO will expedite the imaging process with high pixel resolution.
Project Methods
Objective 1: Develop a microwave UWB and dual-polarized MIT system and machine learning algorithm using simulation models.Task 1.1 Establish simulation models to optimize UWB and dual-polarized MI systemAmong many packaged foods, the research team will focus on three common cases of foreign objects in packaged food - 1) bone in a ground beef, 2) glass in jelly jar, and 3) metal in peanut butter. The purpose of developing the food models is to establish an EM model that represents the impurities in the packaged food and is used to build the UWB and dual-polarized MIT system. These foods and impurities will be modeled in microwave regime by assigning permittivity and conductivity in the commercial EM full-wave solver. An appropriate operating frequency band will be determined to allow sufficient signal penetration through a packaged food model for image generation. The simulation model will be validated with the previous literature and theory. In addition to the food models and the optimal frequency band, transmitting and receiving antenna arrays will be designed with HFSS. First the geometry of a single antenna element which can operate in UWB frequency for improving image quality. To increase accuracy and precisely locate a foreign object, dual polarization will be supported in the antenna. Then, an array will be constructed with the single elements in a circular fashion. The number of antenna array will be determined to obtain appropriate image quality for machine learning algorithms. The radius of the UWB antenna array will be determined based on image resolution required to identify the location and size of impurity.Task 1.2 Learning enhanced image reconstructionWe will develop a learning-based scheme on the top of conventional image reconstruction method. Briefly, we develop a deep learning network to learn a mapping relationship between image dataset obtained from a low-quality setup, and image dataset obtained a high-quality setup. This deep learning will be based on our previous setup to learn low resolution medical images and high resolution medical image. We will adopt residual channel attention block structure, which has been demonstrated with superior performance in reconstructing under-sampled spatial data. The developed algorithm will serve as an image quality enhancement toolbox. During on-site imaging, we will choose a simplified antenna setup to reconstruct an LQ image. Then, the reconstructed image will be sent to the image enhancement software, to generate a HQ image. Noticeably, the image qualify boosting process only takes around ~25 ms per our preliminary study.Task 1.3 Segmentation-based object analysisIn HQ image, we will conduct image analysis by drawing the boundary of foreign objects (i.e., image segmentation). To draw the boundary of objects, we will assign pixel-level label to all pixels in microwave image. On the basis of existing deep learning framework, U-Net, we will customize our segmentation framework in directions: focal loss and transfer learning. Loss function is a critical design in deep learning framework which decide how to efficiently salient features from existing data. Inspired by our previous work, we will set our focal loss as FL(Pt)=-(1-Pt)rlog(Pt) to address the data imbalance issue, which corresponds the fact that only a small number of pixels in microwave image are related to the object we aim to segmentation. Here, Pt is the ground-truth probability of an object. The hyperparameter r modulates the focusing task. Setting r down-weights the loss for easily classified tissue classes (Pt >0.5), putting more focus on classes hard to classify (i.e., the object). Moreover, to make sure the network design can be quickly adapted in packaged food and poultry detection, the neural network will be optimized in a transfer learning manner. In particular, an incremental learning scheme will be adopted to quickly update the deep learning architecture by incrementally updating new labels and data.Objective 2: Assess the performance of the microwave sensing system using packaged food.Task 2.1 Design and build an automated UWB and dual-polarized MIT sensing systemA prototype of the UWB dual-polarized imaging system will be built in the PI's laboratory. The antennas will be fabricated using laser milling machine to precisely realize the antenna designed in Objective 1. The antennas will be characterized in two anechoic chambers including both ETS Lindgren to and Howland 2100 which the PI has access to at UA. The circular array configuration will be made with a flexi glass which is flexible to bend to provide 360o perimeter to hold the antennas. A low-loss and broadband RF switching network will be incorporated to provide all possible signal path channels from one antenna to the other antennas. A micro-controller will provide digital logic signals to adjust switching sequences to capture EM transmission and reflection. The transmitting EM wave will be generated from a vector network analyzer which can support extremely wide frequency band from 10 MHz to 50 GHz. A computer code will be made to allow automatic and accurate data collection of the packaged food and image generation. We will implement our software in a multi-GPU setup to speed up the analysis for the high volume of data acquired in this task. In particular, we will use a multi-GPU computer, which is currently housed in Dr. Gan's lab, to assign the image reconstruction and image processing to separate GPU(s). Distributed measurements for parallel computing will be used as a special plugin in the algorithms developed in Task 1.2 with the sole aim of speeding up the process. The targeted data collection to imaging time is expected to be less than 30 seconds during the movement on the conveying belt. The packaged food will be purchased from grocery store including Publix, Walmart, and Costco in Alabama.Task 2.2 Validate system and algorithm using packaged food samplesThe performance of the developed microwave-based inspection system and machine learning design will be tested on the real packaged food samples. Different types of packaged food will be purchased from the grocery and may be processed into desired samples for evaluation. Packaged food samples with well-defined bone shape or tissue boundary on surface, such as T-bone steak, chicken wings, and pork belly, will be used on the microwave-based system. Images will be generated through reconstruction from the microwave data and compared with visual images taken using a digital camera. For each type of packaged food sample, we will conduct an initial trial from which the number of microwave images required will be determined by power analysis. All microwave images will be manually annotated by drawing the boundary of each tissue guided by visual images. We will conduct five-fold cross-validation on the dataset. The whole dataset will be randomly split to five groups, each with 20% of the images. We iterate the groups by choosing four groups for training and one group for testing. After five iterations, the mean value of Dice coefficient will be used to evaluate the accuracy of segmentation. Priro to our study, the best performance method in segmenting microwave imaginge is a Dice coefficient of 0.9. We expect the proposed method improve the segementation method and achieve a Dice coefficient of 0.94. Under the cross-validation setup, we will have 2736 images for training and 684 images for validation. With a total of 684 images in each validation, we will have a 99% power to detect if the proposed model is significantly better than existing practice using a one-sided one-sample proportional test at a significance level of 5%.Project output evaluationTask 1.1 & 1.2 for Q1, Q2 and Q3Task 1.3 and 1.4 for Q2, Q3, and Q4Task 2.1 for Q4 and Q5Task 2.2 for Q5 to Q7Manuscripts and reports: Q4 to Q8