Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry

• URL: http://arxiv.org/abs/2104.05326v1
• Date: Mon, 12 Apr 2021 10:15:15 GMT
• Title: Unsupervised foreign object detection based on dual-energy absorptiometry in the food industry
• Authors: Vladyslav Andriiashen, Robert van Liere, Tristan van Leeuwen, Kees Joost Batenburg
• Abstract summary: This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA) A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The results show that samples without foreign objects are correctly identified in 97% of cases, the overall accuracy of foreign object detection reaches 95%.
• Score: 2.2175470459999627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: X-ray imaging is a widely used technique for non-destructive inspection of agricultural food products. One application of X-ray imaging is the autonomous, in-line detection of foreign objects in food samples. Examples of such inclusions are bone fragments in meat products, plastic and metal debris in fish, fruit infestations. This article presents a processing methodology for unsupervised foreign object detection based on dual-energy X-ray absorptiometry (DEXA). A foreign object is defined as a fragment of material with different X-ray attenuation properties than those belonging to the food product. A novel thickness correction model is introduced as a pre-processing technique for DEXA data. The aim of the model is to homogenize regions in the image that belong to the food product and enhance contrast where the foreign object is present. In this way, the segmentation of the foreign object is more robust to noise and lack of contrast. The proposed methodology was applied to a dataset of 488 samples of meat products. The samples were acquired from a conveyor belt in a food processing factory. Approximately 60\% of the samples contain foreign objects of different types and sizes, while the rest of the samples are void of foreign objects. The results show that samples without foreign objects are correctly identified in 97% of cases, the overall accuracy of foreign object detection reaches 95%.

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