Affiliation:
1. Toulouse INP, University of Toulouse, Institut de Recherche en Informatique de Toulouse, 31400 Toulouse, France
Abstract
Recent decades have witnessed the development of vision-based dietary assessment (VBDA) systems. These systems generally consist of three main stages: food image analysis, portion estimation, and nutrient derivation. The effectiveness of the initial step is highly dependent on the use of accurate segmentation and image recognition models and the availability of high-quality training datasets. Food image segmentation still faces various challenges, and most existing research focuses mainly on Asian and Western food images. For this reason, this study is based on food images from sub-Saharan Africa, which pose their own problems, such as inter-class similarity and dishes with mixed-class food. This work focuses on the first stage of VBDAs, where we introduce two notable contributions. Firstly, we propose mid-DeepLabv3+, an enhanced food image segmentation model based on DeepLabv3+ with a ResNet50 backbone. Our approach involves adding a middle layer in the decoder path and SimAM after each extracted backbone feature layer. Secondly, we present CamerFood10, the first food image dataset specifically designed for sub-Saharan African food segmentation. It includes 10 classes of the most consumed food items in Cameroon. On our dataset, mid-DeepLabv3+ outperforms benchmark convolutional neural network models for semantic image segmentation, with an mIoU (mean Intersection over Union) of 65.20%, representing a +10.74% improvement over DeepLabv3+ with the same backbone.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference60 articles.
1. World Health Organization (2022). Noncommunicable Diseases: Progress Monitor 2022, World Health Organization.
2. Iriti, M., Varoni, E.M., and Vitalini, S. (2020). Healthy diets and modifiable risk factors for non-communicable diseases—The European perspective. Foods, 9.
3. A survey on food computing;Min;ACM Comput. Surv.,2019
4. A review on vision-based analysis for automatic dietary assessment;Wang;Trends Food Sci. Technol.,2022
5. Vision-based approaches for automatic food recognition and dietary assessment: A survey;Subhi;IEEE Access,2019