Automatic Mackerel Sorting Machine Using Global and Local Features

Yoshito Nagaoka, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Article number8717584
Pages (from-to)63767-63777
Number of pages11
JournalIEEE Access
Publication statusPublished - 2019


  • Convolutional neural networks
  • fish classification
  • fish localization

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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