  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%, and can
simultaneously detect the positions of more than one butterflies in one images
from living environment and recognize the species of those butterflies as well.
