A Deep-Learning-Based Fast Counting Methodology Using Density Estimation for Counting Shrimp Larvae

doi:10.1109/JSEN.2022.3223334

2023-01-01

PRE

DOI：10.1109/JSEN.2022.3223334

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摘要

En 中文

In the process of white shrimp farming, shrimp larvae trading is a time-consuming and laborious process. In the traditional shrimp larvae counting process, it is often necessary to count the number of shrimp larvae for sale through manual counting. However, there is not unified standard method for counting. Computer counting methods can only identify a small number of nonoverlapping shrimp larvae, and the accuracy of the methods is not high. In recent years, with the advent of deep learning, there have been many applications of counting objects. It is desirable to develop a method for counting shrimp larvae that is less labor-intensive and can achieve fast results. In this article, a deep-learning-based fast counting methodology using density estimation for counting shrimp larvae is proposed, with noteworthy advantages over traditional shrimp larvae counting methods. The experimental results showed that the average accuracy of the proposed methodology was as high as 98.72% when the number of shrimps was approximately 1000. Moreover, the average execution time of the proposed methodology was around 12.35 s so that the average execution time can be accepted in the real world.

Keyword:

Network architecture

Sensors

Aquaculture

Deep learning

Feature extraction

Convolutional neural networks

Statistics

computer vision

deep learning

image recognition

image sensor application

intelligent systems

shrimp larvae counting

期刊

IEEE Sensors Journal

IF:

4.5

论文数:

2.2W

被引数:

7.3W

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暂无机构信息

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