SDDNet: Real-Time Crack Segmentation
By Wooram Choi, Young-Jin Cha. IEEE transactions on industrial electronics (1982. Print), 2020
394 citations recorded by the source corpus.
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.
Subfields: Civil and Structural Engineering, Mechanical Engineering
View Paper: https://www.semanticscholar.org/paper/1d41bf398a90910bd19f2b48a214081b9faaa236
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In Civil and Structural Engineering
In Mechanical Engineering
Citations in the corpus
- Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning (2021, methodology): “Choi and Cha [19] observed that when a CNN trained on images of monotonous background and subsequently tested on a more complex dataset the performance drastically decreased; precision dropped from 0.874 to 0.231.”