Open Access

ARTICLE

A Study on Classification and Detection of Small Moths Using CNN Model

Sang-Hyun Lee^*
Department of Computer Engineering, Honam University, Gwangsangu, Gwangju, 62399, South Korea
* Corresponding Author: Sang-Hyun Lee. Email:
(This article belongs to this Special Issue: Emerging Applications of Artificial Intelligence, Machine learning and Data Science)

Computers, Materials & Continua 2022, 71(1), 1987-1998. https://doi.org/10.32604/cmc.2022.022554

Received 11 August 2021; Accepted 16 September 2021; Issue published 03 November 2021

Abstract

Currently, there are many limitations to classify images of small objects. In addition, there are limitations such as error detection due to external factors, and there is also a disadvantage that it is difficult to accurately distinguish between various objects. This paper uses a convolutional neural network (CNN) algorithm to recognize and classify object images of very small moths and obtain precise data images. A convolution neural network algorithm is used for image data classification, and the classified image is transformed into image data to learn the topological structure of the image. To improve the accuracy of the image classification and reduce the loss rate, a parameter for finding a fast-optimal point of image classification is set by a convolutional neural network and a pixel image as a preprocessor. As a result of this study, we applied a convolution neural network algorithm to classify the images of very small moths by capturing precise images of the moths. Experimental results showed that the accuracy of classification of very small moths was more than 90%.

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Keywords

Convolution neural network; rectified linear unit; activation function; pooling; feature map

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