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RAO-UNet: a residual attention and octave UNet for road crack detection via balance loss
Lili Fan, Hongwei Zhao,
Ying Li
, Shen Li, Rui Zhou, Wenbo Chu
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*
此作品的通讯作者
机械与车辆学院
Jilin University
University of Wisconsin-Madison
Waytous Inc.
China Intelligent and Connected Vehicles (Beijing) Research Institute Company Ltd.
科研成果
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期刊稿件
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同行评审
23
引用 (Scopus)
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探究 'RAO-UNet: a residual attention and octave UNet for road crack detection via balance loss' 的科研主题。它们共同构成独一无二的指纹。
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Computer Science
Frequency Feature
100%
Detection Accuracy
50%
Computational Cost
50%
Process Optimization
50%
Detection Result
50%
Processing Speed
50%
Spatial Frequency
50%
Image Frequency
50%
Public Data Set
50%
Optimization Space
50%
Engineering
Road
100%
Crack Detection
100%
Computational Cost
20%
State-of-the-Art Method
20%
Computational Fluid Dynamics
20%
Generated Image
20%
Loss Function
20%
Image Frequency
20%
Spatial Frequency
20%
Missed Detection
20%
Frequency Relationship
20%