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
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.14 No.5
- 발행연도
- 2025.10
- 수록면
- 657 - 667 (11page)
- DOI
- 10.5573/IEIESPC.2025.14.5.657
이용수
초록· 키워드
Given the existing problems such as weak interaction ability, low information recognition rate, and poor accuracy of goods identification, a new system of goods identification based on a multi-modal learning method was designed. By integrating commodity visual, voice, text, and other multiple information, a multi-modal information recognition model is built, commodity data is mapped to the model analysis module using modal retrieval, ITC contrast training, ITM interactive training, and ITG weighted training is carried out, and multi-modal commodity information is initially integrated. SDI+HDMI dual-interface encoder was used to encode the commodity graphic frequency information, and TRIOPC-MCAT-2 controller and IC identifier were selected to optimize the hardware equipment, effectively improve the commodity identification computing power and information response rate, and enhance the stability of the system. The multimodal learning model is used to transform the commodity information into 10-dimensional mapping vectors, multimodal coding is carried out according to the model input hierarchy, key features are extracted using SVM classifier, and multimodal feature information fusion vectors of commodities are obtained through interactive guided weighting operation, and commodity recognition is carried out according to the training results. According to the experimental results, given the massive commodity information of multiple categories, the success rate of the multi-modal learning-based live broadcast cargoes identification system designed in this paper for the commodity multi-modal information fusion recognition has reached more than 88%, the recognition time is less than 90ms, and the recognition accuracy rate is higher than 95%, indicating that the system studied in this paper has good recognition performance. The practical application effect is better than the traditional method, which can meet the current needs of live broadcast goods identification.
#Multimodal learning
#Live streaming with goods
#Goods with goods
#Image recognition
#Recognition system
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- Abstract
- 1. Introduction
- 2. Multimodal Learning Based Merchandise Recognition Model Architecture
- 3. Hardware Design of Live Strip Merchandise Recognition System Based on Multimodal Learning
- 4. Software Design of Live Streaming Product Identification System Based on Multimodal Learning
- 5. Experimental Research
- 6. Conclusion
- References
참고문헌
참고문헌 신청최근 본 자료
UCI(KEPA) : I410-151-26-02-094305329
