Pothole detection in asphalt pavement images

By C. Koch, Ioannis K. Brilakis. Advanced Engineering Informatics, 2011

562 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/fb7046cccf2cdb381312bbff6a52e00835073da1

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In Civil and Structural Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

Asphalt Pavement Pothole Detection and Segmentation Based on Wavelet Energy Field (2017, methodology): “The methods proposed by Ryu et al. [19] and Koch and Brilakis [17] not only need to calculate the geometric criterions of the pothole region, but also need to extract the texture information to identify whether it is a pothole or not.”
A framework for dimensional and surface quality assessment of precast concrete elements using BIM and 3D laser scanning (2015, methodology): “Zhu and Brilakis [62] suggested the use of three circular filters to detect air pockets on the surfaces of concrete.”
Autonomous Structural Visual Inspection Using Region‐Based Deep Learning for Detecting Multiple Damage Types (2018, background): “…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 et al., 2003), and potholes in asphalt pavement (Koch and Brilakis, 2011).”
Automatic Road Crack Detection Using Random Structured Forests (2016, background): “In terms of the current methods [5], [8], [9], [11], [14], [15], most researchers try to suppress the inference of noises by incorporating features such as gray-level value [11], the mean and the standard deviation value [5], [6], [16].”
A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure (2015, background): “[118] also presented a computer vision based approach for pothole detection in asphalt images.”
Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, unknown): “…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 (Koch & Brilakis, 2011; Wang, Zhang, Wang, Braham, & Qiu, 2018; Zhang et al., 2016a).”
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring (2019, methodology): “Numerous techniques exist for the detection and assessment of asphalt pavement cracks and defects using heuristic feature-extraction techniques [99–105].”