Mackerel Fat Content Estimation using RGB and Depth Images
Mackerel Fat Content Estimation using RGB and Depth Images
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DOI：
10.1109/access.2021.3134260
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Shuya Sano;Tomo Miyazaki;Yoshihiro Sugaya;Naohiro Sekiguchi;S. Omachi
中科院分区：
文献类型：
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作者：
Shuya Sano;Tomo Miyazaki;Yoshihiro Sugaya;Naohiro Sekiguchi;S. Omachi
我们提出了一种根据鲭鱼图像估计其脂肪含量的方法。鱼的市场价值根据脂肪含量的不同而有很大差异。例如，脂肪含量高的鲭鱼是日本渔业商业交易的重中之重。脂肪含量通常通过使用近红外光谱的特殊设备手动测量，这增加了成本并降低了生产率。使用普通相机等廉价设备自动估算脂肪含量是理想的选择。然而，从鱼图像估计脂肪含量是一项具有挑战性的任务，因为脂肪含量的差异仅表现为外观上的细微差异。为了解决这个问题，我们建议不仅使用 RGB 图像，还使用深度图像来利用形状信息和纹理。为了检测纹理和形状的细微差异，我们提出了一种卷积神经网络，可以从部分图像（例如鲭鱼图像的头部、身体和尾部）中提取和连接特征。结合分别从 RGB 和深度图像中提取的颜色纹理和三维形状特征来估计脂肪含量。实验结果表明，该方法估计的脂肪含量平均绝对误差为2.25个点。
We propose a method for estimating the fat content of mackerels from their images. The market value of fish varies greatly depending on the fat content. For example, mackerels with high-fat content are a high priority for business transactions in Japanese fisheries. The fat content is commonly measured manually with special equipment using the near-infrared spectroscopy, which increases costs and reduces productivity. It is ideal to estimate the fat content automatically using inexpensive equipment such as ordinary cameras. However, fat content estimation from fish images is a challenging task because the difference in fat content appears only as a slight difference in their appearance. To tackle this problem, we propose to use not only RGB images but also depth images to utilize shape information as well as the textures. To detect subtle differences in texture and shape, we propose a convolutional neural network that extracts and concatenates features from part images, such as the head, body, and tail of a mackerel image. Color-texture and three-dimensional shape features extracted from RGB and depth images, respectively, are combined to estimate the fat content. Experimental results show that the proposed method estimated fat content with 2.25 points at mean absolute error.