Parametric Modeling in Food Package Defect Imaging
A novel approach in food package defect detection is proposed based on system
identification theory where the channel defect detection problem can be regarded as the conventional system
identification problem, i.e., estimation of the system impulse response based on the input-output sequence
using parametric and non-parametric models. The well-known parametric model ARX has been investigated in
this paper. The data are collected with a focused ultrasound transducer (17.3-MHz, 6.35-mm-diameter, f/2,
173-micron, -6 dB pulse-echo lateral beam-width at the focus) scanned over a rectangular grid, keeping the
packages in the focus. Performance is measured in terms of detection rate, image contrast, and
contrast-to-noise ratio. The results using the ARX model are compared to previous image formation
techniques and also compared to the non-parametric method, i.e., spectral analysis. The results show that
the ARX model has the comparable detection rate as RFCS and higher detection rate than BAI and RFS (except
6-micron air-filled channel in plastic trilaminate film) for channel in plastic trilaminate
film. The ARX model has achieved the moderate contrast enhancement and ranks 2nd in
contrast-to-noise ratio enhancement among the compared techniques. The ARX model has a low
detection rate for channel defects in aluminum trilaminate
film, which shows that its performance is material dependent. Finally, the parametric method, ARX model
demonstrates better performance than the non-parametric method, spectral analysis for food package defect
detection.
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