K
Kelvin C. P. Wang
Researcher at Oklahoma State University–Stillwater
Publications - 168
Citations - 3840
Kelvin C. P. Wang is an academic researcher from Oklahoma State University–Stillwater. The author has contributed to research in topics: Pavement engineering & Pavement management. The author has an hindex of 27, co-authored 168 publications receiving 2579 citations. Previous affiliations of Kelvin C. P. Wang include University of Arkansas & Arkansas State University.
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
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.
Journal ArticleDOI
Designs and Implementations of Automated Systems for Pavement Surface Distress Survey
TL;DR: A modified approach to collecting and processing surface distress through the use of high-performance digital cameras for the acquisition of surface distress data is presented, in terms of their potential and applicability.