A deep feature extractor approach for the recognition of pollen-bearing bees
Küçük Resim Yok
Tarih
2020
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this study, a convolutional neural network (ESA) based feature extracting hybrid model was proposed for the identification of bees carrying pollen or not. The fc6 and fc7 layers of AlexNet and VGG16 which a pre-trained ESA architecture, were used as feature extractors. The performances of the different combinations of the deep properties obtained using the SVM classifier were calculated. The PollenDataset dataset was used to test the proposed model. According to the experimental results, an accuracy score of 97.20% was obtained. As a result, the obtained accuracy score was compared with the state-of-the-art accuracy scores and the proposed model provided better performance than the compared methods.
Açıklama
28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK
Anahtar Kelimeler
Object recognition, Convolutional Neural Networks, AlexNet architecture, VGG16 architecture, SVM classifier
Kaynak
2020 28th Signal Processing and Communications Applications Conference (Siu)
WoS Q Değeri
N/A
