Visual recognition for product tracking in slaughterhouses

Visual recognition for product tracking in slaughterhouses


Meat traceability is important for linking process and quality parameters from the individual meat cuts back to the production data from the farmer that produced the animal. Current tracking systems rely on physical tagging, which is too intrusive for individual meat cuts in a slaughterhouse environment. In this article, we demonstrate a computer vision system for recognizing meat cuts at different points along a slaughterhouse production line. More specifically, we show that 211 pig loins can be identified correctly between two photo sessions. The pig loins undergo various perturbation scenarios (hanging, rough treatment and incorrect trimming) and our method is able to handle these perturbations gracefully. This study shows that the suggested vision-based approach to tracking is a promising alternative to the more intrusive methods currently available.


Larsen, A. B. L., Hviid, M. S., Jørgensen, M. E., Larsen, R., & Dahl, A. L. (2014). Vision-based method for tracking meat cuts in slaughterhouses . Meat Science , 96, 366–372. PDF BibTeX


The code is available. Warning: This was one of my first forays into computer vision programming with Python!

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