[1] Zhang Y H. A brief discussion on model selection of water level gauge for mountain river[J]. Automation in Water Resources and Hydrology, 45-46(2008).

[2] Lan H Y, Yan H. Research on application of the scale extraction of water-level ruler based on image recognition technology[J]. Yellow River, 37, 28-30(2015).

[3] Lin R F, Xu H. Automatic measurement method for canals water level based on imaging sensor[J]. Transducer and Microsystem Technologies, 32, 53-55(2013).

[4] Niu Z X, Hu C J, Ruan C et al. The extraction and application of automatic water level monitoring system based on image gauge[J]. Electronic Design Engineering, 27, 103-107(2019).

[5] Zhou H, Zhong S D. Research on water level monitoring based on image processing[J]. Semiconductor Optoelectronics, 40, 390-394, 400(2019).

[6] Huang Z H, Xiong H L, Zhu M et al. Embedded measurement system and interpretation algorithm for water gauge image[J]. Opto-Electronic Engineering, 40, 1-7(2013).

[7] Zhang Z, Zhou Y, Wang H B et al. Image-based water level measurement with standard bicolor staff gauge[J]. Chinese Journal of Scientific Instrument, 39, 236-245(2018).

[8] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).

[9] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).

[10] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015, 9351, 234-241(2015).

[11] Lin G S, Milan A, Shen C H et al. RefineNet: multi-path refinement networks for high-resolution semantic segmentation[C], 5168-5177(2017).

[12] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).

[13] Chen L C, Papandreou G, Kokkinos I et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. https:∥arxiv.org/abs/1412.7062

[14] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).

[15] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. https:∥arxiv.org/abs/1706.05587

[16] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 833-851(2018).

[17] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[18] Roy A G, Navab N, Wachinger C. Concurrent spatial and channel ‘squeeze & excitation’ in fully convolutional networks[M]. Frangi A F, Schnabel J A, Davatzikos C, et al. Medical image computing and computer assisted intervention-MICCAI 2018, 11070, 421-429(2018).

[19] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C], 770-778(2016).

[20] Xu N, Price B, Cohen S et al. Deep image matting[C], 311-320(2017).

[21] Yu C Q, Wang J B, Peng C et al. BiSeNet: bilateral segmentation network for real-time semantic segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11217, 334-349(2018).

[22] Wang L, Chen M E, Meng K K et al. Research on water level recognition method based on deep learning algorithms[J]. Water Resources Informatization, 39-43, 56(2020).