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Alexey N. Beskopylny, Evgenii M. Shcherban?, Sergey A. Stel?makh, Levon R. Mailyan, Besarion Meskhi, Irina Razveeva, Alexey Kozhakin, Diana El?shaeva, Nikita Beskopylny and Gleb Onore
The creation and training of artificial neural networks with a given accuracy makes it possible to identify patterns and hidden relationships between physical and technological parameters in the production of unique building materials, predict mechanical...
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Xinjian Xiang, Haibin Hu, Yi Ding, Yongping Zheng and Shanbao Wu
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detectio...
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Yifan Liu, Weiliang Gao, Tingting Zhao, Zhiyong Wang and Zhihua Wang
The aim of this study is to enhance the efficiency and lower the expense of detecting cracks in large-scale concrete structures. A rapid crack detection method based on deep learning is proposed. A large number of artificial samples from existing concret...
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Xiaohu Zhang and Haifeng Huang
Crack detection is an important task for road maintenance. Currently, convolutional neural-network-based segmentation models with attention blocks have achieved promising results, for the reason that these models can avoid the interference of lights and ...
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Yanjie Zhu, Weidong Xu, C. S. Cai and Wen Xiong
After years of service, bridges could lose their expected functions. Considering the significant number of bridges and the adverse inspecting environment, the urgent requirement for timely and efficient inspection solutions, such as computer vision techn...
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