인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
피인용 0
초록·키워드
This study aimed to identify the most suitable algorithm for accurate and efficient real-time rice quantity detection on white serving trays to enhance personalized nutrition management and improve dietary quality and health. A real-time rice quantity detection system was developed using the DeepLabV3Plus network with various backbone architectures, including DenseNet121, DenseNet201, VGG16, VGG19, MobileNetV2, and MobileNetV3 Large. DeepLabV3Plus employed Atrous Convolutional Neural Networks (ACNNs) and Atrous Spatial Pyramid Pooling (ASPP). A total of 350 images of rice servings were used, with 70% for training and 30% for validation. Performance evaluation of the six backbone architectures revealed that DenseNet121 and DenseNet201 achieved the highest Mean Intersection over Union (mIOU) of 92.92%, while MobileNetV2 had the lowest at 87.70%. VGG16 demonstrated the highest computational efficiency. DenseNet201 was effective in detecting rice quantities up to 140 g, but accurate detection of larger quantities required additional height image data. All architectures achieved high top-1 validation accuracies (96.0%-96.4%), though training times varied significantly. This study emphasizes the importance of diverse training datasets and continuous model refinement to improve segmentation accuracy and efficiency. Integrating image data from top and side views is essential for detecting larger quantities. Enhanced nutrition management using deep learning in institutional food services could lead to increased operational efficiency, cost savings, and data-driven decision-making for inventory and menu planning.
본문·목차
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