Investigating the Effects of Pre-Trained Deep Learning Models and Fusion Techniques on Fruit Segmentation Performance
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Institute of Electrical and Electronics Engineers Inc.
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In recent years, interest in the automation of agricultural and industrial processes has increased. Fruit segmentation is essential for quality control, harvest optimization, and yield estimation. However, some challenges arise in automatic fruit segmentation systems, such as light conditions, image quality, environmental factors, and variations in fruit appearance. To over-come these challenges, we proposed an approach by investigating the effects of pretrained deep learning models with late fusion techniques. Firstly, we designed a late fusion module focusing on the information fusion in the last two layers of the deep learning models. Then, we integrated this late fusion module into the two parallel deep learning models that process the input images independently. Finally, the fruit segmentation output is produced based on the combined information in the late fusion module. The proposed method achieved an IoU score of 79.13% on the Minneapple dataset. Compared to CNN and Transformer models, the proposed method achieved a performance improvement of 9.42% and 3.29%, respectively. © 2024 IEEE.
Açıklama
9th International Conference on Computer Science and Engineering, UBMK 2024 -- 2024-10-26 through 2024-10-28 -- Antalya -- 204906
Anahtar Kelimeler
deep learning, fruit segmentation, fusion techniques
Kaynak
WoS Q Değeri
Scopus Q Değeri
N/A
