The Automatic Identification of Butterfly Species

by   Juanying Xie, et al.

The available butterfly data sets comprise a few limited species, and the images in the data sets are always standard patterns without the images of butterflies in their living environment. To overcome the aforementioned limitations in the butterfly data sets, we build a butterfly data set composed of all species of butterflies in China with 4270 standard pattern images of 1176 butterfly species, and 1425 images from living environment of 111 species. We propose to use the deep learning technique Faster-Rcnn to train an automatic butterfly identification system including butterfly position detection and species recognition. We delete those species with only one living environment image from data set, then partition the rest images from living environment into two subsets, one used as test subset, the other as training subset respectively combined with all standard pattern butterfly images or the standard pattern butterfly images with the same species of the images from living environment. In order to construct the training subset for FasterRcnn, nine methods were adopted to amplifying the images in the training subset including the turning of up and down, and left and right, rotation with different angles, adding noises, blurring, and contrast ratio adjusting etc. Three prediction models were trained. The mAP (Mean Average prediction) criterion was used to evaluate the performance of the prediction model. The experimental results demonstrate that our Faster-Rcnn based butterfly automatic identification system performed well, and its worst mAP is up to 60 simultaneously detect the positions of more than one butterflies in one images from living environment and recognize the species of those butterflies as well.


page 3

page 4

page 7


Diseño y desarrollo de aplicación móvil para la clasificación de flora nativa chilena utilizando redes neuronales convolucionales

Introduction: Mobile apps, through artificial vision, are capable of rec...

The iWildCam 2021 Competition Dataset

Camera traps enable the automatic collection of large quantities of imag...

The iWildCam 2019 Challenge Dataset

Camera Traps (or Wild Cams) enable the automatic collection of large qua...

Learning New Tricks from Old Dogs – Inter-Species, Inter-Tissue Domain Adaptation for Mitotic Figure Assessment

For histopathological tumor assessment, the count of mitotic figures per...

Butterfly detection and classification based on integrated YOLO algorithm

Insects are abundant species on the earth, and the task of identificatio...

Semi-Supervised Recognition of the Diploglossus Millepunctatus Lizard Species using Artificial Vision Algorithms

Animal biometrics is an important requirement for monitoring and conserv...

WhoAmI: An Automatic Tool for Visual Recognition of Tiger and Leopard Individuals in the Wild

Photographs of wild animals in their natural habitats can be recorded un...

Please sign up or login with your details

Forgot password? Click here to reset