Automatic Road Crack Detection Using Random Structured Forests

By Yong Shi, Limeng Cui, Zhiquan Qi, Fan Meng, Zhensong Chen. IEEE transactions on intelligent transportation systems (Print), 2016

1,285 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/ce711e917b9f6a4abd2d3555714a90a280c9fa44

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

1. D. Dais
2. Necati Catbas
3. Fan Yang
4. Vasilis Sarhosis
5. E. Smyrou
6. İ. Bal
7. Chuan-Zhi Dong

Citations in the corpus

• Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, background): “Thus, background pixels predicted as cracks (FP) were considered as TP if they were a few pixels apart from the annotated cracks [56,76 – 78].”
• Ensemble of Deep Convolutional Neural Networks for Automatic Pavement Crack Detection and Measurement (2020, methodology): “ experimental result images for different methods are shown in Figure 5 (from left to right: Coatings 2020, 10, 152 9 of 15 original image, ground truth, Canny [13], local threshold [12], CrackForest [24], structured prediction [29], U-net [47], and the proposed method.). It can be observed that noi”
• Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection (2019, methodology): “For instance, Shi et al [2] successfully apply structure forest [21], a classical edge detection method, for crack detection.”
• A review of computer vision–based structural health monitoring at local and global levels (2020, methodology): “Random Forest: Shi et al.(54) Deep learning CNN for image classification (new developed): Zhang et al.”
• DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection (2019, background): “the minimal spanning tree [45] and the random structure forest [46].”
• Surface Defect Detection Methods for Industrial Products: A Review (2021, background): “com/Iskysir/Bridge_Crack_Image_Data (accessed on 21 June 2021) Crack Forest Datasets [132,133] Road crack https://github.”
• Automated pavement crack detection and segmentation based on two‐step convolutional neural network (2020, methodology): “CrackForest dataset (CFD)was added into the pavement crack detection dataset, which consists of 118 images with 480 × 320 pixels in size captured by a cell phone in Beijing, China (Shi et al., 2016).”