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

TL;DR

A novel thickness correction model is introduced as a pre-processing technique for DEXA data to homogenize regions in the image that belong to the food product and enhance contrast where the foreign object is present.

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%.

Figures (9)

• Figure 1: Diagram of the foreign object inspection procedure. The input is two projections of the sample acquired with different voltages of the tube. The blue curve on the images approximately shows the sample boundary. The segmentation image uses green color for pixels that were incorrectly classified as a defect, red - for not detected foreign object pixels, and yellow - for detected pixels of the defect.
• Figure 2: Correlation between absorption of skeletal muscle for the X-ray tube voltages of 40 and 90 kV(a). The ratio between the attenuation rate is drawn as a function of thickness. The quotient is not constant due to a polychromatic spectrum (b).
• Figure 3: Sample scan with low exposure (0.5 s per projection): quotient image computed as a division of two projections (a) and the dependency of quotient values on the single projection intensity (b).
• Figure 4: Sample scan with high exposure (5 s per projection): quotient image computed as a division of two projections (a) and the dependency of quotient values on the single projection intensity (b).
• Figure 5: Stages of the scan data pre-processing: quotient image computed as a division of 2 channels as in Eq. \ref{['quotient_formula']} (a), quotient after thickness correction defined by Eq. \ref{['corrected_quotient']} (b), quotient after correction and normalization computed according to Eq. \ref{['normalied_quotient']} (c). The sample is a chicken fillet with a fan bone scanned with an exposure time of 0.5 s.