DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
By Qin Zou, Zheng Zhang, Qingquan Li, Xianbiao Qi, Qian Wang, Song Wang. IEEE Transactions on Image Processing, 2019
912 citations recorded by the source corpus.
Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder–decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves $F$ -measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.
Subfields: Civil and Structural Engineering
View Paper: https://www.semanticscholar.org/paper/3cd7b551ce6e96298429060506732478a58a2478
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In Civil and Structural Engineering
Citations in the corpus
- Automated pavement crack detection and segmentation based on two‐step convolutional neural network (2020, methodology): “D. Zhang et al. (2018) proposed a sparse processing automated pavement distress detection algorithm based on laser scanning 3D data.”
- Encoder–decoder network for pixel‐level road crack detection in black‐box images (2019, methodology): “Zou et al. (2019) proposed a method for detecting cracks in pavement and stone surface images using an encoder–decoder network.”
- Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks (2019, background): “…notice that deep learning as an emerging technology has demonstrated state-of-the-art, human-competitive, and sometimes better-than-human performance in solving many computer vision problems such as object detection [10]–[12], image classification/retrieval [13] and semantic segmentation [14]–[17].”
- CrackU‐net: A novel deep convolutional neural network for pixelwise pavement crack detection (2020, background): “Zou et al. 32 proposed a crack detection model called DeepCrack, which can learn the hierarchical features of the cracks effectively.”