Abstract:
The task of recognizing non-Western and non-
Chinese food items as well as accurately segmenting food item
instances is a seldom researched and challenging task in the
field of Computer Vision. Food items such as Sri Lankan short
eats snacks have high inter-class visual similarity, mainly in
terms of color and the fact that food images are highly prone
to occlusion or item overlap where a portion of an object is
hidden from sight. Existing databases are few and synthetic and
current systems do not handle food item occlusion. In this paper
a novel Sri Lankan short eats food item instance segmentation
and amodal completion approach is introduced as well as two
novel datasets for Sri Lankan short eats instance segmentation
and amodal instance segmentation. The proposed method shows
model performance improvements up to 88.4% mAP in Instance
Segmentation and up to 90% mIoU in Amodal Completion, as
well as the advantage of real-time inference in less than 1.7
seconds per frame.
