Efficient Pavement Crack Detection in Drone Images using Deep Neural Networks
- Authors
- Kim, H.; Sung, J.; Kim, M.; Park, C.; Paik, J.
- Issue Date
- 2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- crack detection; sigmoid fusion module; YOLOX
- Citation
- 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
- Journal Title
- 2023 International Conference on Electronics, Information, and Communication, ICEIC 2023
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67632
- DOI
- 10.1109/ICEIC57457.2023.10049911
- ISSN
- 0000-0000
- 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.
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Collections - Graduate School of Advanced Imaging Sciences, Multimedia and Film > Department of Imaging Science and Arts > 1. Journal Articles
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