Water Level Monitoring Method Based on Semantic Segmentation

Qifan Fu; ming Lu; Zhiyi Zhang; Li Ji; Huaze Ding

doi:10.3788/LOP202259.0410004

Laser & Optoelectronics Progress, Volume. 59, Issue 4, 0410004(2022)

Water Level Monitoring Method Based on Semantic Segmentation

Qifan Fu^1,2, ming Lu^1,2, Zhiyi Zhang¹, Li Ji¹, and Huaze Ding^1、*

¹Key Laboratory of Wireless Sensor Network and Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China

²University of Chinese Academy of Sciences, Beijing 100864, China

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Figures & Tables(14)

Fig. 1. Structure of improved DeepLabv3+ model

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Fig. 2. Residual structure of ResNet

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Fig. 3. Residual structure with attention mechanism

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Fig. 4. Structure of edge refinement module

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Fig. 5. Effect comparison of edge refinement module before and after modification. (a) Original image; (b) before correction; (c) after correction

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Fig. 6. Image of water level scale taken. (a) Scale of water level is not clear; (b) calibration cannot be observed

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Fig. 7. Effect comparison of different semantic segmentation algorithms on water scale segmentation. (a) Original image; (b) ground truth; (c) U-Net; (d) PSPNet; (e) DeepLabv3+; (f) proposed algorithm

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Fig. 8. Horizontal plane detection effect comparison of different algorithms. (a) Original image; (b) algorithm 1; (c) algorithm 2; (d) proposed algorithm

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Fig. 9. Test results of algorithm under different extreme environmental conditions. (a) Low light environment; (b) strong light environment; (c) foreign body occlusion; (d) blurred water level scales images; (e) calm and clear reflection of water; (f) nighttime water level scale images

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Table 1. Performance comparison of different semantic segmentation algorithms for water level scale segmentation
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Table 1. Performance comparison of different semantic segmentation algorithms for water level scale segmentation
Algorithm mIoU /% IoU /% MPA /%
SegNet 89.63 81.87 92.01
U-Net 92.77 86.91 94.46
RefineNet 93.58 88.63 95.36
PSPNet 95.31 91.89 97.98
BiSeNet 95.57 92.37 98.12
DeepLabv3+ 95.96 93.26 98.69
Proposed algorithm 97.18 95.03 99.22

Table 2. Experimental results of ablation by introducing channel attention, spatial attention, and edge thinning module
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Table 2. Experimental results of ablation by introducing channel attention, spatial attention, and edge thinning module
Edge refinement cSE sSE mIoU /% MPA /%
95.96 98.69
√ 96.27 98.75
√ 96.64 98.93
√ √ 96.86 99.03
√ 96.71 98.86
√ √ 96.85 98.92
√ √ 97.02 99.14
√ √ √ 97.18 99.22

Table 3. Horizontal detection effect comparison of semantic segmentation algorithm
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Table 3. Horizontal detection effect comparison of semantic segmentation algorithm
Algorithm Pixel error Pixel error rate /%
SegNet 13.38 3.18
U-Net 8.04 2.09
RefineNet 7.96 2.02
PSPNet 5.94 1.38
BiSeNet 6.63 1.55
DeepLabv3+ 5.48 1.22
Proposed algorithm 3.73 0.76

Table 4. Comparison of horizontal detection effects of different water level measurement algorithms
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Table 4. Comparison of horizontal detection effects of different water level measurement algorithms
Algorithm Pixel error Pixel error rate /%
Algorithm 1 51.47 11.03
Algorithm 2 14.61 3.72
Proposed algorithm 3.73 0.76

Table 5. Comparison and analysis of water level read by algorithm and manual

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Table 5. Comparison and analysis of water level read by algorithm and manual

Moment	Manual reading /m	Algorithmic reading /m	Error /m	Moment	Manual reading /m	Algorithmic reading /m	Error /m
0：00	0.863	0.867	0.004	12：00	0.808	0.814	0.006
1：00	0.851	0.850	-0.001	13：00	0.795	0.798	0.003
2：00	0.848	0.852	0.004	14：00	0.786	0.792	0.006
3：00	0.845	0.850	0.005	15：00	0.789	0.794	0.005
4：00	0.837	0.846	0.009	16：00	0.793	0.798	0.005
5：00	0.834	0.830	-0.004	17：00	0.810	0.807	-0.003
6：00	0.828	0.819	-0.009	18：00	0.813	0.818	0.005
7：00	0.818	0.813	-0.005	19：00	0.815	0.809	-0.006
8：00	0.821	0.820	-0.001	20：00	0.831	0.822	-0.009
9：00	0.822	0.827	0.005	21：00	0.848	0.834	-0.014
10：00	0.826	0.819	-0.007	22：00	0.862	0.856	-0.006
11：00	0.803	0.800	-0.003	23：00	0.864	0.846	-0.018

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Qifan Fu, ming Lu, Zhiyi Zhang, Li Ji, Huaze Ding. Water Level Monitoring Method Based on Semantic Segmentation[J]. Laser & Optoelectronics Progress, 2022, 59(4): 0410004

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Paper Information

Category: Image Processing

Received: Jan. 22, 2021

Accepted: Mar. 22, 2021

Published Online: Jan. 25, 2022

The Author Email: Huaze Ding (dinghz@mail.sim.ac.cn)

DOI:10.3788/LOP202259.0410004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Performance comparison of different semantic segmentation algorithms for water level scale segmentation

Table 1. Performance comparison of different semantic segmentation algorithms for water level scale segmentation

Table 2. Experimental results of ablation by introducing channel attention, spatial attention, and edge thinning module

Table 2. Experimental results of ablation by introducing channel attention, spatial attention, and edge thinning module

Table 3. Horizontal detection effect comparison of semantic segmentation algorithm

Table 3. Horizontal detection effect comparison of semantic segmentation algorithm

Table 4. Comparison of horizontal detection effects of different water level measurement algorithms

Table 4. Comparison of horizontal detection effects of different water level measurement algorithms

Table 5. Comparison and analysis of water level read by algorithm and manual

Table 5. Comparison and analysis of water level read by algorithm and manual