The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
SDDNet: Real-Time Crack Segmentation
Open accessAbstract
This article reports the development of a pure deep learning method for segmenting concrete cracks in images. The objectives are to achieve the real-time performance while effectively negating a wide range of various complex backgrounds and crack-like features. To achieve the goals, an original convolutional neural network is proposed. The model consists of standard convolutions, densely connected separable convolution modules, a modified atrous spatial pyramid pooling module, and a decoder module. The semantic damage detection network (SDDNet) is trained on a manually created crack dataset, and the trained network records the mean intersection-over-union of 0.846 on the test set. Each test image is analyzed, and the representative segmentation results are presented. The results show that the SDDNet segments cracks effectively unless the features are too faint. The proposed model is also compared with the most recent models, which show that it returns better evaluation metrics even though its number of parameters is 88 times less than in the compared models. In addition, the model processes in real-time (36 FPS) images at 1025 × 512 pixels, which is 46 times faster than in a recent work.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Fully convolutional networks for semantic segmentation
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Cited by
Deep Learning‐Based Crack Damage Detection Using Convolutional Neural Networks
Cha, Y., Choi, W., & Büyüköztürk, O. (2017) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1111/mice.12263 · Cited by 3,295
Self-citationDais, D., Bal, İ. E., Smyrou, E., & Sarhosis, V. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103606 · Cited by 429
Jia, J., & Li, Y. (2023) · Sensors · doi:10.3390/s23218824 · Cited by 182
Performance Comparison of Multiple Convolutional Neural Networks for Concrete Defects Classification
Arafin, P., Issa, A., & Billah, A. H. M. M. (2022) · Sensors · doi:10.3390/s22228714 · Cited by 34
Metadata
- Authors
- Choi, W., & Cha, Y.-J.
- Venue
- IEEE Transactions on Industrial Electronics
- Published
- 2020-09
- Volume
- 67
- Issue
- 9
- Pages
- 8016-8025
- ISSN
- 0278-0046 (ISSN-L)
- 1557-9948
- OpenAlex
- W2979396152
- Cited by
- 462
- References
- 44
- FWCI
- 20.18
- Type
- journal article
- Language
- en
- Open access
- green
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