Vision-based detection of loosened bolts using the Hough transform and support vector machines
By Young-Jin Cha, Kisung You, Wooram Choi. 2016
308 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Media Technology
View Paper: https://www.semanticscholar.org/paper/58200b79278a1ec54b938d09362fb3c7ccdefd24
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In Computer Vision and Pattern Recognition
In Electrical and Electronic Engineering
In Media Technology
Citations in the corpus
- Autonomous UAVs for Structural Health Monitoring Using Deep Learning and an Ultrasonic Beacon System with Geo‐Tagging (2018, background): “To overcome these issues, many computer vision-based noncontact sensing techniques have been developed (Abdel-Qader et al., 2003; Lee and Shinozuka, 2006; Chen et al., 2015; Cha et al., 2016).”
- Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types (2018, background): “…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., 2016), road pavement cracks (Ying and Salari, 2010; Cord and Chambon, 2012; Zalama et al., 2014), underground concrete pipe cracks (Sinha…”
- Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, unknown): “The Support Vector Machine (SVM) has been used for detecting cracks (Gavilán et al., 2011; O'Byrne, Schoefs, Ghosh, & Pakrashi, 2013; Zou, Cao, Li, Mao, & Wang, 2012), loose bolts (Cha, You, & Choi, 2016), rusting (Chen, Shen, Lei, & Chang, 2012), and spalling (German et al., 2012).”