Cheng, J., Xiong, W., Chen, W., Gu, Y., & Li, Y. (2018). Pixel-level Crack Detection using U-Net. TENCON 2018 - 2018 IEEE Region 10 Conference, 0462–0466. https://doi.org/10.1109/tencon.2018.8650059
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.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done