In-line image analysis in the slaughter industry, illustrated by Beef Carcass Classification

• Published in: Meat science Vol. 43; pp. 151 - 163
• Main Authors: Borggaard, C., Madsen, N.T., Thodberg, H.H.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Oxford Elsevier Ltd 1996; Elsevier
• Subjects: automation; beef; Biological and medical sciences; carcass composition; classification; Food industries; Fundamental and applied biological sciences. Psychology; image analysis; livestock and meat industry; Meat and meat product industries; principal component analysis; quality controls; quantitative analysis; slaughter; Europe; Automation; Three dimensional shape; Pattern recognition; Neural network; Carcass; Image analysis; Technology; Classification; Quality control; Computerized processing; Beef; Slaughterhouse; Principal component analysis
• ISSN: 0309-1740; 1873-4138
• DOI: 10.1016/0309-1740(96)00062-9

This paper describes a complete framework for the quantitative quality control of biological objects using computer vision. The techniques are described in the context of BCC-2, the second generation Beef Carcass Classification centre, which has operated as a prototype since March 1995. Installed in the slaughterline, BCC-2 analyses one half, dehided carcass. BCC-2 determines the visual properties: conformation, fatness and fat colour as well as objective quantities such as the percent of saleable meat and the cross sectional area of the rib eye.
BCC-2 measures geometry and colour quantitatively. A procedure for maintaining the same calibration over time for several BCC-2 units has been developed. BCC-2 is built from a few inexpensive components: A frame that positions the half carcass in the slaughterline, a camera, two PC's, and a terminal. In addition, two slide projectors project stripes of light onto the carcass at an angle to the camera to provide information about the three-dimensional shape. The biological variation of the carcasses requires the use of advanced information processing techniques: traditional pattern recognition, principal component analysis, and neural networks. BCC-2 is adaptive, i.e. it is trained by examples, and BCC-2 is robust in the sense that it classifies all carcasses except the ones most damaged in the slaughter process.