A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 6
IEEE/CAA Journal of Automatica Sinica
| Citation: | Y. Lin, Z. Z. Xu, D. Chen, Z. J. Ai, Y. Qiu, and Y. Z. Yuan, “Wood crack detection based on data-driven semantic segmentation network,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1510–1512, Jun. 2023. doi: 10.1109/JAS.2023.123357
|
| [1] |
K. Gu, Y. Zhang, and J. Qiao, “Vision-based monitoring of flare soot,” IEEE Trans. Instrum. Meas., vol. 69, no. 9, pp. 7136–7145, 2020. doi: 10.1109/TIM.2020.2978921
|
| [2] |
G. A. Ruz, P. A. Estevez, and C. A. Perez, “A neurofuzzy color image segmentation method for wood surface defect detection,” For. Prod. J., vol. 55, no. 4, pp. 52–58, 2005.
|
| [3] |
W. Luo and L. Sun, “An improved binarization algorithm of wood image defect segmentation based on non-uniform background,” J. For. Res., vol. 30, no. 4, pp. 1527–1533, 2019. doi: 10.1007/s11676-019-00925-w
|
| [4] |
Z. Chang, J. Cao, and Y. Zhang, “A novel image segmentation approach for wood plate surface defect classification through convex optimization,” J. For. Res., vol. 29, no. 6, pp. 1789–1795, 2018. doi: 10.1007/s11676-017-0572-7
|
| [5] |
Q. Chen, Z. Liu, X. Ma, and Y. Wang, “Artificial neural correlation analysis for performance-indicator-related nonlinear process monitoring,” IEEE Trans. Ind. Informat., vol. 18, no. 2, pp. 1039–1049, 2021.
|
| [6] |
F. Li, T. Zheng, N. He, and Q. Cao, “Data-driven hybrid neural fuzzy network and ARX modeling approach to practical industrial process identification,” IEEE/CAA J. Autom. Sinica, vol. 9, no. 9, pp. 1702–1705, 2022. doi: 10.1109/JAS.2022.105821
|
| [7] |
T. He, Y. Liu, Y. Yu, Q. Zhao, and Z. Hu, “Application of deep convolutional neural network on feature extraction and detection of wood defects,” Measurement, vol. 152, p. 107357, 2020. doi: 10.1016/j.measurement.2019.107357
|
| [8] |
Y. Tu, Z. Ling, S. Guo, and H. Wen, “An accurate and real-time surface defects detection method for sawn lumber,” IEEE Trans. Instrum. Meas., vol. 70, pp. 1–11, 2020.
|
| [9] |
Y. Lin, D. Chen, S. Liang, Z. Xu, Y. Qiu, J. Zhang, and X. Liu, “Color classification of wooden boards based on machine vision and the clustering algorithm,” Appl. Sci., vol. 10, no. 19, p. 6816, 2020.
|
| [10] |
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. 18th Int. Conf. Med. Image Comput. Comput.-Assist. Intervent, Springer, 2015, pp. 234–241.
|
| [11] |
K. Liu, Z. Ye, H. Guo, D. Cao, L. Chen, and F.-Y. Wang, “Fiss GAN: A generative adversarial network for foggy image semantic segmentation,” IEEE/CAA J. Autom. Sinica, vol. 8, no. 8, pp. 1428–1439, 2021. doi: 10.1109/JAS.2021.1004057
|
| [12] |
G. Doğan and B. Ergen, “A new mobile convolutional neural network-based approach for pixel-wise road surface crack detection,” Measurement, vol. 195, p. 111119, 2022. doi: 10.1016/j.measurement.2022.111119
|
| [13] |
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2017, pp. 2881–2890.
|
| [14] |
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proc. Eur. Conf. Comput. Vis., 2018, pp. 801–818.
|
| [15] |
Y. Liu, J. Yao, X. Lu, R. Xie, and L. Li, “Deepcrack: A deep hierarchical feature learning architecture for crack segmentation,” Neurocomputing, vol. 338, pp. 139–153, 2019. doi: 10.1016/j.neucom.2019.01.036
|
| [16] |
L. Yang, H. Wang, B. Huo, F. Li, and Y. Liu, “An automatic welding defect location algorithm based on deep learning,” NDT E Int., vol. 120, p. 102435, 2021. doi: 10.1016/j.ndteint.2021.102435
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