Food Detection and Recognition with Deep Learning: A Comparative Study: A Comparative Study

Siao Wah Tan, Chin Poo Lee, Kian Ming Lim, Jit Yan Lim

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

Food detection and recognition involves the use of computer vision and machine learning techniques to identify and classify food items in images or videos. It has numerous applications, such as dietary tracking, nutrition analysis, and inventory management. This research paper presents a comparative study of six deep learning models (SSD (VGG-16), Faster-RCNN (Resnet-50), Faster-RCNN (Mobilenet-V3), Faster-RCNN (Mobilenet-V3-320), RetinaNet (Resnet-50), and YOLOv5) for food detection and recognition. The models' performance is evaluated using three publicly available datasets: School Lunch Dataset, UEC FOOD 100, and UEC FOOD 256. Notably, Faster R-CNN (Mobilenet-V3) achieved mAP of 0.931 in the School Lunch Dataset, while YOLOv5 achieved 0.774 and 0.701 mAP in the UEC FOOD 100 and UEC FOOD 256 Datasets, respectively. YOLOv5 demonstrates comparable results to Faster R-CNN but with a smaller input image size and a larger batch size in food detection.

Original languageEnglish
Title of host publication2023 11th International Conference on Information and Communication Technology, ICoICT 2023
PublisherIEEE Computer Society
Pages283-288
Number of pages6
ISBN (Electronic)9798350321982
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event11th International Conference on Information and Communication Technology, ICoICT 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023

Publication series

NameInternational Conference on ICT Convergence
Volume2023-August
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference11th International Conference on Information and Communication Technology, ICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23

Keywords

  • Faster Region-Based Convolutional Neural Networks (Faster R-CNN)
  • Food detection
  • Object detection
  • YOLOv5

ASJC Scopus subject areas

  • Information Systems
  • Computer Networks and Communications

Fingerprint

Dive into the research topics of 'Food Detection and Recognition with Deep Learning: A Comparative Study: A Comparative Study'. Together they form a unique fingerprint.

Cite this