Convolutional Neural Network-Based Fish Posture Classification

Xin Li,Wenguang Hou,Wenjin Wu,Anzi Ding,Shaojie Mei,Danilo Comminiello

doi:10.1155/2021/9939688

Abstract

Fish killing machines can effectively relieve the workers from the backbreaking labour. Generally, it is necessary to ensure the fish to be in unified posture before being input into the automatic fish killing machine. As such, how to detect the actual posture of fish in real time is a new and meaningful issue. Considering that in the actual situation, we only need to determine the four postures which are related to the head, tail, back, and belly of the fish, and we transfer this task into a four-kind classification problem. As such, the convolutional neural network (CNN) is introduced here to do classification and then to detect the fish’s posture. Before training the network, all sample images are preprocessed to make the fish be horizontal on the image according to the principal component analysis. Meanwhile, the histogram equalization is used to make the grey distribution of different images be close. After that, two kinds of strategies are taken to do classification. The first is a paired binary classification CNN and the second is a four-category CNN. In addition, three kinds of CNN are adopted. By comparison, the four-kind classification can obtain better results with error less than 1/1000.

Highlights

Fish is one of the most important foods for human beings
Our team is trying to make an automatic fish input device, which is used to adjust the fish posture to make fish be in uniform orientation before inputting into the machine [3]. erefore, it is necessary to detect the actual orientation of the fish on the conveyor belt in real time and adjust it
The fish should be horizontally located on the image. erefore, the first step of image preprocessing is to rotate the image, so that the fish is basically in a horizontal status in the rotated image. It is to generate the similar samples as the actual case. e basic principle is to perform principal component analyze (PCA) to the original image [7]

Summary

Introduction

Fish is one of the most important foods for human beings. At present, lot of manual dissection is needed in making fish production. e production quality and processing speed depend on the worker’s proficiency and efficiency. Erefore, the first step of image preprocessing is to rotate the image, so that the fish is basically in a horizontal status in the rotated image It is to generate the similar samples as the actual case. E main calculation process is as follows: (a) binarize the original image on basis of the Otsu algorithm [8]; roughly divide the image into two kinds: target and background; (b) set the fish as the high grey (target) and the background as the low grey level; (c) the coordinates of all the target after binarization are formed into a matrix:. It can be seen that the fish is horizontally located in the image, and the grey distribution is relatively uniform, i.e., the samples for CNN training is obtained.

Paired Binary Classification

Four-Type Classification

Findings

Conclusion