Abstract
Pork is the most commonly consumed meat across the world: about one-third of all meat consumed is pork, ahead of beef and chicken. Every day a massive number of pig carcasses enter the production pipeline. When entering the pipeline, the carcasses are graded by the slaughterhouses to determine the market price of the meat. The grading criteria could depend on a variety of factors such as the tenderness, color, pH value, water holding capacity as well as the proportion of red meat inside the carcasses. Since the grade can be used to determine the market price and the commercial usage of the meat, this process is crucial. Unfortunately, the grading process is not only time consuming but also requires expertise. To mitigate this problem, in this work we propose: 1) a pig carcass image dataset segmented by experts, 2) an LSQ index image dataset and 3) an algorithm for carcass quality analysis based on the ratio of red meat inside the carcasses, or the Lenden-Speck-Quotient (LSQ). Our experimental results demonstrate that the performance of the proposed LSQ index algorithm is reliable and agrees with experts' annotation with MAPE 5.55%.
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| Dataset |
Description |
| Raw images dataset |
There are 312 images of pig carcasses taken by mobile phone camera. The images are 450 by 600
pixels. All the images reveal the left-side of the pig half-carcass including several parts of the
meat e.g., leg, loin and spareribs. |
| Segmentation image dataset |
The dataset contains segmented images of pig carcasses from the Raw images dataset. These images are
manually segmented by 3 experts into 5 categories: fat, red meat, spinal canal, other meat, and
background |