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
- 발행연도
- 2024.12
- 수록면
- 398 - 404 (7page)
- DOI
- 10.33162/JAR.2024.12.24.4.398
이용수
초록· 키워드
오류제보하기
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.
목차
1. 서론
2. 분류 기법
3. 데이터 셋
4. 모델 학습 및 비교 분석
5. 결론
References
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