Automatic Mackerel Sorting Machine Using Global and Local Features

Abstract

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. Firstly, it needs localization of mackerels on a conveyor belt so that mackerels can be transported to destinations. Secondly, 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 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 the features makes the classification accurate and robust. Experimental results show that the proposed classification is superior to the methods using either features of irradiated or whole body regions. Moreover, we confirmed that the automatic mackerel-sorting machine performs accurately.