Efficient Pavement Crack Detection in Drone Images using Deep Neural Networks
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Sung, J. | - |
dc.contributor.author | Kim, M. | - |
dc.contributor.author | Park, C. | - |
dc.contributor.author | Paik, J. | - |
dc.date.accessioned | 2023-09-15T02:48:58Z | - |
dc.date.available | 2023-09-15T02:48:58Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 0000-0000 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67632 | - |
dc.description.abstract | Since the drone input images are usually filmed at very high altitudes and with various viewing angles, cracks appear in many different sizes and shapes. A commonly used crack detection dataset has a small size such as 480x320, and it is important that how well it detects the prominent continuous cracks. To detect pavement cracks, we proposed an efficient crack detection network. The proposed network achieved better performance than the conventional network. © 2023 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Efficient Pavement Crack Detection in Drone Images using Deep Neural Networks | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICEIC57457.2023.10049911 | - |
dc.identifier.bibliographicCitation | 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85150415740 | - |
dc.citation.title | 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023 | - |
dc.type.docType | Conference Paper | - |
dc.subject.keywordAuthor | crack detection | - |
dc.subject.keywordAuthor | sigmoid fusion module | - |
dc.subject.keywordAuthor | YOLOX | - |
dc.description.journalRegisteredClass | scopus | - |
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