On-Line Automated Inspection of Poultry Carcasses by Machine Vision
Development of an automated poultry inspection system that is low-cost, operates 
with minimum human intervention, and is able to maintain its accuracy is important to 
the U.S. Food Safety and Inspection Service (FSIS) and the poultry industry. Such 
a system, placed strategically in the processing plants, would help improve the 
inspection speed, minimize problems of human error and variability, improve the 
effectiveness of the federal inspection program, and increase the slaughter plants’ 
productivity. This paper describes an automated poultry inspection system that 
utilizes machine vision technology. The system was tested on-line at a poultry plant. 
The results showed that the classification accuracy for wholesome and unwholesome 
carcasses were 94 and 87%, respectively.
Intensified Multispectral Imaging System for Poultry Carcass Inspection
A multispectral imaging system possessing six optical filters at 542, 570, 641, 700, 720, and 847 nm was demonstrated feasible for discriminating normal poultry carcasses from abnormal carcasses. Gray level image intensities of selected wavelengths were used to classify normal carcasses from abnormal carcasses. Visible wavelengths, 542, 570, and 641 nm, were useful for separation of normal carcasses from septicemic and cadaver carcasses. The accuracy was 89.3% for separation of normal carcasses from abnormal carcasses. The accuracy for separation of the normal carcasses from the septicemic was 89.3%, and from cadaver, 96.5%. Among the abnormal carcasses, the accuracy of classification of the septicemic carcasses was 90.0% and of the cadaver carcasses, 96.6%. The accuracies of classification for poultry carcasses into three classes using a neural networks classifier were 92.9% for normal, 83.4% for septicemic, and 96.6% for cadaver carcasses.
Real-time color grading and defect detection of food products
Manufacturing processes that utilize natural products as raw materials for forming their deliverables face additional challenges in the areas of quality control and inspection. This comes about from the natural variability that occurs in the products. Systems to automate this activity have been difficult to design and implement from the view of algorithm complexity which impacts the computational requirements for real-time execution. This paper will describe a technique for recognizing global or systematic defects on poultry carcasses. A method for implementing the technique that is capable of executing at a rate of about 180 birds per minute is described.