8 November 2005 3D sensing for machine guidance in meat cutting applications
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Abstract
Most cutting and deboning operations in meat processing require accurate cuts be made to obtain maximum yield and ensure food safety. This is a significant concern for purveyors of deboned product. This task is made more difficult by the variability that is present in most natural products. The specific application of interest in this paper is the production of deboned poultry breast. This is typically obtained from a cut of the broiler called a 'front half' that includes the breast and the wings. The deboning operation typically consists of a cut that starts at the shoulder joint and then continues along the scapula. Attentive humans with training do a very good job of making this cut. The breast meat is then removed by pulling on the wings. Inaccurate cuts lead to poor yield (amount of boneless meat obtained relative to the weight of the whole carcass) and increase the probability that bone fragments might end up in the product. As equipment designers seek to automate the deboning operation, the cutting task has been a significant obstacle to developing automation that maximizes yield without generating unacceptable levels of bone fragments. The current solution is to sort the bone-in product into different weight ranges and then to adjust the deboning machines to the average of these weight ranges. We propose an approach for obtaining key cut points by extrapolation from external reference points based on the anatomy of the bird. We show that this approach can be implemented using a stereo imaging system, and the accuracy in locating the cut points of interest is significantly improved. This should result in more accurate cuts and with this concomitantly improved yield while reducing the incidence of bones. We also believe the approach could be extended to the processing of other species.
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Wayne Daley, Doug Britton, Colin Usher, Mamadou Diao, Kevin Ruffin, "3D sensing for machine guidance in meat cutting applications", Proc. SPIE 5996, Optical Sensors and Sensing Systems for Natural Resources and Food Safety and Quality, 59960D (8 November 2005); doi: 10.1117/12.632239; https://doi.org/10.1117/12.632239
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