Name
Abstract | ||
|---|---|---|
In this paper, we proposed an automatic crack detection method based on deep convolutional neural network-U-Net [4]. Unlike existing machine learning based crack detection methods, we can process an image as a whole without patchifying, thanks to the encoder-decoder structure of U-Net. The segmentation result is output from the network as a whole, instead of aggregation from neighborhood patches. In addition, a new cost function based on distance transform is introduced to assign pixel-level weight according to the minimal distance to the ground truth segmentation. In experiments, we test the proposed method on two datasets of road crack images. The pixel-level segmentation accuracy is above 92% which outperforms other state-of-the-art methods significantly. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/tencon.2018.8650059 | TENCON IEEE Region 10 Conference Proceedings |
Keywords | Field | DocType |
U-Net,convolutional neural network,crack detection,crack segmentation | Pattern recognition,Segmentation,Computer science,Convolutional neural network,Image segmentation,Electronic engineering,Distance transform,Ground truth,Pixel,Artificial intelligence | Conference |
ISSN | Citations | PageRank |
2159-3442 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jierong Cheng | 1 | 1 | 2.07 |
| Wei Xiong | 2 | 23 | 6.75 |
| Wenyu Chen | 3 | 11 | 4.23 |
| Ying Gu | 4 | 22 | 9.45 |
| Yusha Li | 5 | 0 | 2.03 |