Concrete Crack Detection by Multiple Sequential Image Filtering

By Takafumi Nishikawa, Junji Yoshida, Toshiyuki Sugiyama, Yozo Fujino. Comput. Aided Civ. Infrastructure Eng., 2012

353 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/de23c4f9714ce4df5e27add495c0248651921675

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In Civil and Structural Engineering

In Ocean Engineering

Citations in the corpus

Synchrosqueezed wavelet transform-fractality model for locating, detecting, and quantifying damage in smart highrise building structures (2015, methodology): “Most of the current damage detection methods applied for SHM are visual or localized experimental procedures such as laser scanning (Park et al 2007), acoustic emission (Li et al 2011a, Farhidzadeh et al 2013), image processing (Nishikawa et al 2012), among others.”
Road Damage Detection and Classification Using Deep Neural Networks with Smartphone Images (2018, methodology): “Although many excellent methods have been proposed, such as segmentation of cracks on the concrete surface (O’Byrne et al., 2014; Nishikawa et al., 2012), our research uses an object detection method.”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, background): “…detect some speciﬁc types of structural damage, such as concrete cracks (Abdel-Qader, Abudayyeh, & Kelly, 2003; Fujita, Mitani, & Hamamoto, 2006; Nishikawa, Yoshida, Sugiyama, & Fujino, 2012), concrete spalling (German, Brilakis, & DesRoches, 2012), and potholes and cracks in asphalt pavement…”
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types (2018, methodology): “…situations using pre-and postprocessing methods to detect one speciﬁc type of structural damage, such as concrete cracks (Abdel-Qader et al., 2003; Nishikawa et al., 2012), concrete spalling (German et al., 2012), steel cracks (Yeum and Dyke, 2015), loosened bolts (Park et al., 2015; Cha et al.,…”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, background): “[74] proposed multiple sequential image filtering for crack detection and property estimation.”
Automated Crack Detection on Concrete Bridges (2016, methodology): “For example, a genetic programming algorithm [8] shows results with 6 training and 17 test images”