Technical University of Denmark, Lyngby, Denmark.
Meat Sci. 2014 Jan;96(1):366-72. doi: 10.1016/j.meatsci.2013.07.023. Epub 2013 Jul 23.
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.
肉类可追溯性对于将个体肉块的加工和质量参数与生产该动物的农场主的生产数据联系起来非常重要。当前的跟踪系统依赖于物理标记,对于屠宰场环境中的个体肉块来说,这种方法过于侵入性。在本文中,我们展示了一种用于在屠宰场生产线上的不同点识别肉块的计算机视觉系统。更具体地说,我们展示了可以在两个拍摄时段之间正确识别 211 个猪里脊肉。猪里脊肉经历了各种干扰场景(悬挂、粗暴处理和不正确的修剪),我们的方法能够优雅地处理这些干扰。这项研究表明,与当前可用的更具侵入性的方法相比,基于视觉的跟踪方法是一种很有前途的替代方法。