The segmented UEC Food-100 dataset with benchmark experiment on food detection
Küçük Resim Yok
Tarih
2023
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Automatic food classification systems have several interesting applications ranging from detecting eating habits, to waste food management and advertisement. When a food image has multiple food items, the food detection step is necessary before classification. This work challenges the food detection issue and it introduces to the research community the Segmented UEC Food-100 dataset, which expands the original UEC Food-100 database with segmentation masks. In the semantic segmentation experiment, the performance of YOLAC and DeeplabV3+ has been compared and YOLAC reached the best accuracy of 64.63% mIoU. In the instance segmentation experiment, YOLACT has been used due to its speed and high accuracy. The benchmark performance on the newly released Segmented UEC Food-100 dataset is 68.83% mAP. For comparison purpose, experiments have been run also on the UEC FoodPix Complete dataset of Okamoto et al. The database and the code will be available after publication.
Açıklama
Anahtar Kelimeler
Segmented Uec Food-100 Database, Food Detection, Semantic Segmentation, Instance Segmentation
Kaynak
Multimedia Systems
WoS Q Değeri
N/A
Scopus Q Değeri
Q1
Cilt
29
Sayı
4
