Automatic Mackerel Sorting Machine Using Global and Local Features
Automatic Mackerel Sorting Machine Using Global and Local Features
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DOI：
10.1109/access.2019.2917554
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发表时间：
2019-05
期刊：
影响因子：
3.9
通讯作者：
Yoshito Nagaoka;Tomo Miyazaki;Yoshihiro Sugaya;S. Omachi
中科院分区：
文献类型：
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作者：
Yoshito Nagaoka;Tomo Miyazaki;Yoshihiro Sugaya;S. Omachi
在日本，蓝鲭和鲢鱼经常同时捕捞，它们的市场价格也不一样。人类需要手动分类，这需要繁重的劳动。对自动分拣机的需求正在增加。本论文的目的是研制一种鲭鱼自动分选机，这是一个具有挑战性的课题。有两个必要的功能。首先，它需要在传送带上定位鲭鱼，以便将鲭鱼运送到目的地。第二，物种分类是必要的，但由于物种之间的相似外观，这是困难的。在本文中，我们提出了一种使用深度神经网络和红色激光的自动分拣机。具体来说，我们将红色激光照射到腹部，激光的形状将分别为蓝色和椭圆形。我们拍摄图像并使用神经网络来定位整个身体和辐射区域。然后，我们分类鲭鱼使用从全身和照射区域提取的特征。使用这两个特征使得分类准确且鲁棒。实验结果表明，所提出的分类方法优于使用辐射或全身区域的特征的方法是上级。此外，我们证实了自动鲭鱼分选机的准确性。
In Japan, blue and chub mackerels are often caught simultaneously, and their market prices are different. Humans need to sort them manually, which requires heavy labor. The demand for automatic sorting machines is increasing. The aim of this paper is to develop an automatic sorting machine of mackerels, which is a challenging task. There are two required functions. First, it needs localization of mackerels on a conveyor belt so that mackerels can be transported to destinations. Second, species classification is needed, but it is difficult due to similar appearance among the species. In this paper, we propose an automatic sorting machine using deep neural networks and a red laser light. Specifically, we irradiate red laser to the abdomen, and the shape of the laser will be circle and ellipse on the blue and chub mackerels, respectively. We take images and use neural networks to locate the whole body and irradiated regions. Then, we classify mackerels using features extracted from the whole body and irradiated regions. Using both features makes the classification accurate and robust. The experimental results show that the proposed classification is superior to the methods using either feature of irradiated or whole body regions. Moreover, we confirmed that the automatic mackerel-sorting machine performs accurately.