TY  - JOUR
EP  - 48482
SN  - 13807501
PB  - Springer
N1  - cited By 0
SP  - 48457
TI  - Object detection and classification of butterflies using efficient CNN and pre-trained deep convolutional neural networks
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175376045&doi=10.1007%2fs11042-023-17563-4&partnerID=40&md5=ac1f5d63dc24353c02272c7dc76c5a41
JF  - Multimedia Tools and Applications
A1  - Mattins, R.F.
A1  - Sarobin, M.V.R.
A1  - Aziz, A.A.
A1  - Srivarshan, S.
VL  - 83
Y1  - 2024///
N2  - With over 18,000 species, butterflies account for nearly one-quarter of all identified species on the planet. The images of different butterfly species can be utilized to train Deep Convolutional Neural Networks (CNNs) for the automatic detection and classification of butterflies. This work proposes an end-to-end system for automatically detecting butterflies in given images and predicting their respective species. To achieve butterfly detection, we utilized the YOLOv3 object detection model, which was trained on the Beautiful Butterflies dataset. This dataset comprises 832 photos of butterflies from 10 different species, captured from various angles. For species classification, we designed a deep convolutional neural network-based architecture named Efficient Convolutional Neural Network (Effi-CNN), employing multiple CNN layers and trained on the custom dataset. To benchmark the performance of Effi-CNN, we compared three versions: Effi-CNN-1, Effi-CNN-2, and Effi-CNN-3, with five other transfer learning CNN models, including VGG16, VGG19, ResNet50, MobileNetV2, and Inception-v3 models. Evaluation of the models was conducted using a separate test dataset. The YOLOv3 object detection model exhibited a promising result, achieving a mean Average Precision (mAP) of 0.98. Among the classification models, Effi-CNN-3 demonstrated the highest accuracy, reaching 98.20. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
IS  - 16
KW  - Benchmarking; Classification (of information); Convolution; Convolutional neural networks; Deep neural networks; Learning systems; Multilayer neural networks; Object detection; Object recognition; Statistical tests; Transfer learning
KW  -  Automatic classification; Automatic Detection; Butterfly species; Convolutional neural network; Detection models; Images classification; Object classification; Objects detection; Performances analysis; Transfer learning
KW  -  Image classification
ID  - scholars19716
ER  -