시각 장애인을 위한 상품 영양 정보 안내 시스템
Product Nutrition Information System for Visually Impaired People
0
조회
0
다운로드
1
인용
0
참고문헌
키워드
초록
Nutrition information about food is written on the label paper, which is very inconvenient for visually impaired people to recognize. In order to solve the inconvenience of visually impaired people with nutritional information recognition, this paper proposes a product nutrition information guide system for visually impaired people. In the proposed system, user’s image data input through UI, and object recognition is carried out through YOLO v5. The proposed system is a system that provides voice guidance on the names and nutrition information of recognized products. This paper constructs a new dataset that augments the 319 classes of canned/late-night snack product image data using rotate matrix techniques, pepper noise, and salt noise techniques. The proposed system compared and analyzed the performance of YOLO v5n, YOLO v5m, and YOLO v5l models through hyperparameter tuning and learned the dataset built with YOLO v5n models. This paper compares and analyzes the performance of the proposed system with that of previous studies.
Abstract
Nutrition information about food is written on the label paper, which is very inconvenient for visually impaired people to recognize. In order to solve the inconvenience of visually impaired people with nutritional information recognition, this paper proposes a product nutrition information guide system for visually impaired people. In the proposed system, user’s image data input through UI, and object recognition is carried out through YOLO v5. The proposed system is a system that provides voice guidance on the names and nutrition information of recognized products. This paper constructs a new dataset that augments the 319 classes of canned/late-night snack product image data using rotate matrix techniques, pepper noise, and salt noise techniques. The proposed system compared and analyzed the performance of YOLO v5n, YOLO v5m, and YOLO v5l models through hyperparameter tuning and learned the dataset built with YOLO v5n models. This paper compares and analyzes the performance of the proposed system with that of previous studies.
참고문헌 (18)
- 이상현, "객체인식 기술을 활용한 시각장애 보조 사물탐지 및 음성안내 시스템 구현", 전자공학회논문지, 55(11), pp.65~71 (2018), 대한전자공학회
- Investigation of Consumer Issues in Braille Labeling of Food for the Visually Impaired, "J. T. Park", Korea Consumer Agency, 1~66 (2022)
- 박동연, "시각장애인에게 상품을 안내하기 위한 객체 식별 알고리즘", 한국콘텐츠학회 논문지, 22(10), pp.1~10 (2022), 한국콘텐츠학회[DOI]
- 홍승희, "대학생에서 야식의 섭취가 영양소 섭취 상태에 미치는 영향", 동아시아식생활학회지, 23(3), pp.297~310 (2013), 동아시아식생활학회
- 서윤석, "한국 성인의 야식의 에너지 수준에 따른 영양상태 비교: 2005년 국민건강영양조사 자료 이용", Journal of Nutrition and Health, 45(5), pp.479~488 (2012), 한국영양학회[DOI]
- Y. W. Park, "A Product Voice Guidance Service for the Visually Impaired Using Real-time Image Processing Technology Based on Deep Learning", Proceedings of Korea Information and Communications Society Conference, 76(1), pp.126~127 (2021)
- S. Virtue, "GTTs and ITTs in Mice:Simple Tests, Complex Answers", Nature Metabolism, 3(7), pp.1~4 (2021)
- J. Redmon, "You Only Look Once: Unified, Real-Time Object Detection", IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.779~788 (2016)
- 이용환, "객체 검출을 위한 CNN과 YOLO 성능 비교 실험", 반도체디스플레이기술학회지, 19(1), pp.85~92 (2020), 한국반도체디스플레이기술학회
- J. W. Park, "A Study on Deep Learning Performance Improvement Based on YOLOv5", Proceedings of the Korean Institute of Communication Sciences Conference, pp.1592~1593 (2022)
- S. H. Han, "Convenience Store Product Recognition Application for the Blind", Proceedings of the Korea Information Processing Society Conference, 28(2), pp.1298~1301 (2021)
- Z. Eaton-Rosen, "Improving Data Augmentation for Medical Image Segmentation", 1st Conference on Medical Imaging with Deep Learning (MIDL 2018), pp.53~ (2018)
- S. H. Park, "Trends in Data Augmentation Techniques for Deep Learning Models", Proceedings of Symposium of the Korean Institute of communications and Information Sciences, pp.1051~1052 (2021)
- B. Kwon, "A Data Augmentation Approach to 28GHz Path Loss Modeling Using CNNs", 2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp.825~829 (2023)
- S. AbuSalim, "Data Augmentation on Intra-Oral Images Using Image Manipulation Techniques", 2022International Conference on Digital Transformation and Intelligence (ICDI), pp.117~120 (2022)
- 박민준, "무인기로 취득한 RGB 영상과 YOLOv5를 이용한 수수 이삭 탐지", 한국농림기상학회지, 24(4), pp.295~304 (2022), 한국농림기상학회