Automatic road crack segmentation using entropy and image dynamic thresholding
By Henrique Oliveira, P. Correia. European Signal Processing Conference, 2009
328 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/1f37c5542db39b6a5e7c0c2e2b0a77e4ba00a51e
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- Zhun Fan
- Shengyuan Li
- Xincong Yang
- Chong Li
- Xuefeng Zhao
- Yong Shi
- Heng Li
- Guangyi Zhou
Citations in the corpus
- Automatic Pixel‐Level Crack Detection and Measurement Using Fully Convolutional Network (2018, methodology): “In respect to morphology, it is possible to use the structural features to detect cracks, such as histogram and threshold (Oliveira and Correia, 2009); Kirschke and Velinsky (1992) developed a histogram-based method to identify and locate small cracks on asphalt and cement concrete pavements; Cheng…”
- Automatic Road Crack Detection Using Random Structured Forests (2016, background): “In this section, we first give a brief review of crack detection, after that, the related crack characterization methods are discussed.”
- Automated Pixel‐Level Pavement Crack Detection on 3D Asphalt Surfaces Using a Deep‐Learning Network (2017, background): “Thresholding algorithms were proposed to find cracks by setting global or local thresholds (Cheng et al., 2003; Oliveira and Correia, 2009), with various limitations for images with complex illuminance.”
- CrackTree: Automatic crack detection from pavement images (2012, methodology): “In (Oliveira and Correia, 2009), entropy is embedded into a two-level thresholding framework for pavement crack detection.”
- Automated pavement crack detection and segmentation based on two‐step convolutional neural network (2020, unknown): “Although the recall of proposed method is lower by a small margin than that achieved by (Oliveira & Correia, 2009), (e) CrackForest (Shi et al., 2016), (f) method of Jenkins and colleagues (Jenkins, Carr, Insa-Iglesias, Buggy, & Morison, 2018), (g) method of Nguyen and colleagues (Nguyen, Le,…”
- Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network (2019, methodology): “With respect to morphology, the simplest method for detecting damages is to use the histogram and threshold (Kirschke & Velinsky, 1992; Oliveira & Correia, 2009).”
- Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement (2020, methodology): “ults with different methods based on AigleRN. The experimental result images for different methods are shown in Figure 6 (from left to right: original image, ground truth, Canny [13], local threshold [12], Free-Form Anisotropy (FFA) [19], Minimal Path Selection (MPS) [18], structured prediction [29]”