数字农科院2.0

Marbling-Net: A Novel Intelligent Framework for Pork Marbling Segmentation Using Images from Smartphones

文献类型： 外文期刊

作者： Shufeng Zhang;Yuxi Chen;Weizhen Liu;Bang Liu;Xiang Zhou

作者机构：

关键词： context encoder network;image segmentation;patch-based training;pork marbling;pork quality evaluation

期刊名称： Sensors

ISSN： 1424-8220

年卷期： 2023 年 23 卷 11 期

页码：

收录情况： SCIE（2023版） ; ; EI（2023版）

摘要： Marbling characteristics are important traits for the genetic improvement of pork quality. Accurate marbling segmentation is the prerequisite for the quantification of these traits. However, the marbling targets are small and thin with dissimilar sizes and shapes and scattered in pork, complicating the segmentation task. Here, we proposed a deep learning-based pipeline, a shallow context encoder network (Marbling-Net) with the usage of patch-based training strategy and image up-sampling to accurately segment marbling regions from images of pork longissimus dorsi (LD) collected by smartphones. A total of 173 images of pork LD were acquired from different pigs and released as a pixel-wise annotation marbling dataset, the pork marbling dataset 2023 (PMD2023). The proposed pipeline achieved an IoU of 76.8%, a precision of 87.8%, a recall of 86.0%, and an F1-score of 86.9% on PMD2023, outperforming the state-of-art counterparts. The marbling ratios in 100 images of pork LD are highly correlated with marbling scores and intramuscular fat content measured by the spectrometer method ((Formula presented.) = 0.884 and 0.733, respectively), demonstrating the reliability of our method. The trained model could be deployed in mobile platforms to accurately quantify pork marbling characteristics, benefiting the pork quality breeding and meat industry.

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