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
- 학술대회자료
- 저자정보
- 발행연도
- 2023.6
- 수록면
- 1,309 - 1,313 (5page)
이용수
초록· 키워드
오류제보하기
In this paper, we present a novel pipeline for crack classification using a multi-task model. Our model consists of two branches: a classification branch that distinguishes between crack and no crack images, and a segmentation branch that locates the crack region in the image. By combining these two tasks, our model can focus more on the crack itself and avoid being distracted by the surrounding regions. We also use the segmentation output to extract shape features from the crack region and combine them with the classification output to obtain a final crack score. This way, we can reduce the false positive rate significantly and achieve stateof-the-art results on various public datasets. We demonstrate that our multi-task model for crack identification is simple yet effective, achieving 87.29% and 90.14% Average Precision on SDNet2018 and Concrete Crack Images for Classification datasets, recursively.
목차
Abstract
Ⅰ. Introduction
Ⅱ. Body
Ⅲ. Implementation
Ⅳ. Conclusions and Future Research Directions
References
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