Vision-based classification of mosquito species: data augmentation by background replacement for convolutional neural network-based species classification of smashed mosquitoes

Abstract

This study proposes a method of data augmentation by background replacement for the species classification of smashed mosquitoes using convolutional neural networks (CNNs). To augment data from a limited number of images of smashed mosquitoes, varieties of foreground mosquito and background are ensured by clipping a foreground mosquito image and pasting it into different backgrounds. For the background images, a white image is prepared as the ideal background, and a hand palm image is assumed as the background for practical use. Images extracted from three publicly available datasets are also prepared, which are considered as the variable backgrounds. A CNN-based deep classification is used with three types of architecture, and the classification accuracy is compared using training images corresponding to different background conditions. The classification accuracy using training images with a variety of backgrounds is better than that with a white or palm background. Moreover, deep classification with a residual network achieves the highest classification accuracy. The results of this work show that the species classification of the smashed mosquitoes can be achieved by using datasets with the proposed data augmentation method.