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Yue Fei
Researcher at Oklahoma State University–Stillwater
Publications - 12
Citations - 1291
Yue Fei is an academic researcher from Oklahoma State University–Stillwater. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 7, co-authored 11 publications receiving 663 citations.
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Journal ArticleDOI
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep-Learning Network
Allen Zhang,Allen Zhang,Kelvin C. P. Wang,Kelvin C. P. Wang,Baoxian Li,Enhui Yang,Xianxing Dai,Yi Peng,Yue Fei,Yang Liu,Joshua Q. Li,Cheng Chen +11 more
TL;DR: The CrackNet, an efficient architecture based on the Convolutional Neural Network, is proposed in this article for automated pavement crack detection on 3D asphalt surfaces with explicit objective of pixel‐perfect accuracy.
Journal ArticleDOI
Automated Pixel-Level Pavement Crack Detection on 3D Asphalt Surfaces with a Recurrent Neural Network
Allen Zhang,Kelvin C. P. Wang,Kelvin C. P. Wang,Yue Fei,Yang Liu,Cheng Chen,Guangwei Yang,Joshua Q. Li,Enhui Yang,Shi Qiu +9 more
TL;DR: A new recurrent unit, gated recurrent multilayer perceptron (GRMLP), is proposed to recursively update the internal memory of CrackNet‐R, a recurrent neural network for fully automated pixel‐level crack detection on three‐dimensional asphalt pavement surfaces.
Journal ArticleDOI
Pixel-Level Cracking Detection on 3D Asphalt Pavement Images Through Deep-Learning- Based CrackNet-V
Yue Fei,Kelvin C. P. Wang,Allen Zhang,Cheng Chen,Joshua Q. Li,Yang Liu,Guangwei Yang,Baoxian Li +7 more
TL;DR: It is shown that CrackNet-V yields better overall performance particularly in detecting fine cracks compared with CrackNet, and further reveals the advantages of deep learning techniques for automated pixel-level pavement crack detection.
Journal ArticleDOI
Deep Learning–Based Fully Automated Pavement Crack Detection on 3D Asphalt Surfaces with an Improved CrackNet
TL;DR: CrackNet is the result of an 18-month collaboration within a 10-person team to develop a deep learning–based pavement crack detection software that demonstrated successes in terms of accuracy, efficiency, and efficiency.