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
- Laser & Optoelectronics Progress
- Vol. 59, Issue 4, 0410004 (2022)
Fig. 1. Structure of improved DeepLabv3+ model
Fig. 2. Residual structure of ResNet
Fig. 3. Residual structure with attention mechanism
Fig. 4. Structure of edge refinement module
Fig. 5. Effect comparison of edge refinement module before and after modification. (a) Original image; (b) before correction; (c) after correction
Fig. 6. Image of water level scale taken. (a) Scale of water level is not clear; (b) calibration cannot be observed
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
Fig. 8. Horizontal plane detection effect comparison of different algorithms. (a) Original image; (b) algorithm 1; (c) algorithm 2; (d) proposed algorithm
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 2. Experimental results of ablation by introducing channel attention, spatial attention, and edge thinning module
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Table 3. Horizontal detection effect comparison of semantic segmentation algorithm
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Table 4. Comparison of horizontal detection effects of different water level measurement algorithms
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Table 5. Comparison and analysis of water level read by algorithm and manual
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