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.
피인용 0
초록·키워드
Purpose: The current study was used to propose a model for identifying cracks in concrete structures by comparing and analyzing various models using Convolutional Neural Networks (CNNs).
Methods: Two CNN-based classification models, VGG-16 and ResNet-50, were developed, compared, and analyzed. A confusion matrix was employed as a performance indicator to evaluate their performance in individual instances.
Results: The comparative analysis indicated that ResNet-50 outperformed VGG-16 in performance metrics. Additionally, the inference speed based on test data revealed a significant difference, with ResNet-50 requiring 35 seconds compared to VGG-16's 77 seconds.
Conclusion: The ResNet-50 showed excellent performance in confusion matrix-based performance indicators and inference speed. It shows strong potential for practical applications in identifying concrete crack structures in real-world scenarios.
Methods: Two CNN-based classification models, VGG-16 and ResNet-50, were developed, compared, and analyzed. A confusion matrix was employed as a performance indicator to evaluate their performance in individual instances.
Results: The comparative analysis indicated that ResNet-50 outperformed VGG-16 in performance metrics. Additionally, the inference speed based on test data revealed a significant difference, with ResNet-50 requiring 35 seconds compared to VGG-16's 77 seconds.
Conclusion: The ResNet-50 showed excellent performance in confusion matrix-based performance indicators and inference speed. It shows strong potential for practical applications in identifying concrete crack structures in real-world scenarios.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-151-25-02-091262082
