Hong Guan’s research while affiliated with Griffith University and other places

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Publications (1)


Flowchart of the proposed method
(a) A sample crack image, (b) local horizontal variation, and (c) local vertical variation
A sample crack image and the results of local structure extraction
An example crack image and its 10 probability‐based texture cluster maps
Sample crack image and the generated global distribution map

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Foreground–background separation technique for crack detection
  • Article
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December 2018

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49 Citations

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Hong Guan

Current level‐2 condition assessment methods for critical infrastructure assets mostly rely on human visual investigation of visible damages and patterns at the structure surface, which can be a costly, time‐consuming, and subjective exercise in reality. In this article, a novel method for crack detection is proposed via salient structure extraction from textured background. This method first extracts strong edges and distinguishes them from strong textures in a local neighborhood. Then, the spatial distribution of texture features is estimated to detect cracks as salient structures that are not widely spread across the whole image. The outputs from these two key steps are fused to calculate the final structure saliency map for generation of the crack masks. This method was validated on a data set with 704 images and the outcome revealed an average f‐measure of 75% in detecting the concrete cracks that is significantly higher than two other baseline methods.

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Citations (1)


... This method avoids the need to learn the upsampling process, but the quality of pooling indices limits its accuracy. Nayyeri et al. [23] proposed a crack detection method that separates background and foreground based on visual saliency, but it tends to miss tiny cracks. Zhu et al. [24] discovered that surface cracks in materials possess fractal characteristics, and their distribution can be effectively characterized using the concept of fractal dimension. ...

Reference:

Pavement Crack Detection Using Fractal Dimension and Semi-Supervised Learning
Foreground–background separation technique for crack detection