Parametric modeling in food package defect imaging

A novel approach in food package defect detection is proposed based on system identification theory, in which 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 nonparametric 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 microm -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 with previous image formation techniques and also compared with 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-microm air-filled channel in plastic trilaminate film) for channel in plastic trilaminate film. The ARX model has achieved the moderate contrast enhancement and ranks second 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.