The classification of beef carcasses is a task performed by a human expert, where the characteristics of a piece of meat are analyzed visually before being processed. The price and classification of the meat that comes from the inspected piece will depend on this inspection. It is a subjective task based on a visual review carried out by the operator in charge and based on his experience. Factors, such as the lighting of the room, the volume of work, and the type of pieces, can influence the decision of the operator. Currently, there are few and costly automatic systems used to classify beef carcasses. In this document, we propose the design of a computer-vision system in combination with a sensorization system for the real-time classification of beef carcasses. For the first step, Landmark detection techniques are applied for the detection of characteristic points. These points enable the segmentation of the beef carcass. In the second phase, different filters and threshold values are used on the image to segment the fat and proceed to its classification. A case study is carried out that compares the classification of 140 pieces made automatically with the classification of the same parts by a group of human experts with highly relevant results.

牛肉car体的分类是由人类专家执行的任务，在此过程中，肉的特性在加工之前会进行肉眼分析。来自被检查件的肉的价格和分类将取决于该检查。这是主观任务，基于负责人员的视觉检查并根据他的经验。诸如房间的照明，工作量和物品类型等因素会影响操作员的决定。当前，很少有且昂贵的用于对牛肉car体进行分类的自动系统。在本文档中，我们建议结合视觉系统对牛肉car体进行实时分类的计算机视觉系统的设计。第一步 地标检测技术被应用于特征点的检测。这些点可以分割牛肉car体。在第二阶段，在图像上使用不同的过滤器和阈值来分割脂肪并进行分类。进行了一个案例研究，将一组自动完成的140件产品的分类与一组人类专家对相同零件的分类进行了比较，并得出了高度相关的结果。