E
Enhui Yang
Researcher at Southwest Jiaotong University
Publications - 41
Citations - 1205
Enhui Yang is an academic researcher from Southwest Jiaotong University. The author has contributed to research in topics: Asphalt & Convolutional neural network. The author has an hindex of 8, co-authored 32 publications receiving 636 citations. Previous affiliations of Enhui Yang include Oklahoma State University–Stillwater.
Papers
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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
Automatic classification of pavement crack using deep convolutional neural network
Baoxian Li,Kelvin C. P. Wang,Kelvin C. P. Wang,Allen Zhang,Allen Zhang,Enhui Yang,Guolong Wang +6 more
TL;DR: This paper proposes a novel method using deep CNN to automatically classify image patches cropped from 3D pavement images, and finds that the size of receptive field has a slight effect on the classification accuracy.
Journal ArticleDOI
Pixel-level pavement crack segmentation with encoder-decoder network
TL;DR: Instead of fitting crack images directly with ground-truth images, EDNet’s encoder fits crack images with corresponding feature maps to overcome the quantity imbalance problem and Experimental results show that EDNet outperforms other state-of-the-art models.
Journal ArticleDOI
Asphalt Concrete Layer to Support Track Slab of High-Speed Railway:
TL;DR: In this article, the authors focused on the material composition and mechanical response of railway asphalt concrete (RAC) on the basis of mechanistic models and laboratory experiments and found that the air voids of the material RAC-25 used for railway infrastructure should be controlled from 1% to 3%.