Study on Improved YOLOv7-based Algorithm for Detecting Crack Defects in Ancient Buildings-Reference-Cited by-同舟云学术

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Study on Improved YOLOv7-based Algorithm for Detecting Crack Defects in Ancient Buildings

Published:2024-01-26 Issue: Volume:46 Page:194-199
ISSN:
Container-title:Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering
language:
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Author:

Fang Tianke¹^ORCID,Hui Zhenxing²^ORCID,Rey William P¹^ORCID,Xie Zhiying²^ORCID,Yang Aihua³^ORCID,He Yuanrong²^ORCID

Affiliation:

1. School of Information Technology, Mapua University, Philippines

2. Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, China

3. School of Information Media, Zhangzhou Vocational College of Science and Technology, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3672758.3672790

Reference14 articles.

1. Ning Peng. Research on building crack detection based on YOLOv5 algorithm [D]. Jilin University of Architecture, 2023.

2. Tingting Wang Hongtao Wang Zhixian Huang Rock crack detection and identification in outcrop area based on improved YOLOv7[J]. Geoscience and Technology Bulletin:1-14[2023-10-29].

3. Su Weiguo, Wang Jingxiao. Research on road crack recognition model based on YOLO v3 deep learning algorithm[J]. Chinese and foreign highway, 2023, 43(02):58-63.

4. Cheng Wang. Research and application of pavement crack detection based on deep learning and multi-scale analysis [D]. Wuhan University of Technology, 2020.

5. LIAO Xiangchan, LI Cailin, YAO Yukai Crack detection method for highway bridges based on improved YOLO V5[J]. Journal of Shandong University of Technology (Natural Science Edition), 2023, 37(04):1-7.

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