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

作　　者：Sang-Hyun Lee 

机构地区：[1]Department of Computer Engineering,Honam University,Gwangsangu,Gwangju,62399,South Korea

出　　处：《Computers, Materials & Continua》2022年第4期1987-1998,共12页计算机、材料和连续体（英文）

摘　　要：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%.

关 键 词：Convolution neural network rectified linear unit activation function pooling feature map 

分 类 号：S476.3[农业科学—农业昆虫与害虫防治]