Improving Performance of Real-time Multispectral Imaging System

Abstract

A real-time multispectral imaging system has demonstrated a science-based tool for fecal and ingesta contaminant detection during poultry processing. In order to implement this imaging system at commercial poultry processing industry, the false positives must be removed. For doing this, we tested and implemented additional image processing algorithms including binning, cuticle removal filter, median filter, and morphological analysis in real-time mode to maximize detection accuracy and minimize false positives (FPs). The median filtering and binning process were able to reduce FPs up to 98.7% and 95.2%, respectively by eliminating most salt and pepper noise from the raw images. The detection accuracy varied with parameter values of image processing algorithms including binning, threshold, median filter, and morphological filter. Overall contaminant detection accuracy on moving birds varied from 84.3% to 97.8%. In this case, the FPs errors were 1.9% and 41.8%, respectively. Although neither the overall detection accuracy nor FPs errors were affected by camera gains, the results of detection accuracy were slightly changed from 87.4% to 95.1%. In this case, the FPs errors were 1.8% and 15.9%, respectively. Thus, the ARS multispectral imaging system was able to detect contaminants with 91.6% accuracy and 3.3% FPs errors by selecting optimum image processing methods at the processing speed of 140 birds per minute.