Semantic segmentation algorithm based on class feature attention mechanism fusion

Table 1. Performance of different base networks
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Table 1. Performance of different base networks
Base network MIoU/% Param/MB Time/s
Mobilenetv2 74.13 14.9 260.79
Xception 76.58 162.4 308.22
ResNet34 72.24 89.1 257.13
ResNet50 81.34 107.5 267.34
ResNet101 79.09 179.6 347.61

Table 2. Module ablation experiment
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Table 2. Module ablation experiment
网络结构添加不同模块的性能 MIoU/%
Baseline ASPP MASPP CFAM FCA
√ √ 76.59
√ √ 77.13
√ √ 78.83
√ √ √ √ 81.34

Table 3. Classic network performance comparison
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Table 3. Classic network performance comparison
Module MPA/% MIoU/% Param/MB Time/s
SegNet 80.13 70.98 121.3 261.59
DenseASPP 82.68 74.64 12.4 237.14
Deeplabv3+ 85.37 76.59 156.6 285.56
ACFNet 86.61 78.98 124.7 293.38
CFANet 88.32 81.34 107.5 267.34

Table 4. Performance of different networks
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Table 4. Performance of different networks
Module MPA/% MIoU/% Param/MB Time/s
文献［17］ 85.32 74.28 641.00
文献［19］ 76.31 142.28 302.70
文献［26］ 89.34 81.12
CFANet 88.32 81.34 107.5 267.34

Table 5. Performance testing on the Cityscapes dataset

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Table 5. Performance testing on the Cityscapes dataset

类别	Deeplabv3+/%	CFANet/%	类别	Deeplabv3+/%	CFANet/%
Road	97.78	98.79	Sky	89.79	91.37
Sidewalk	84.13	85.18	Person	76.71	78.62
Building	89.09	91.71	Rider	61.56	65.73
Wall	61.48	63.83	Car	90.28	90.35
Fence	56.77	60.81	Truck	54.58	57.72
Pole	58.97	59.06	Bus	77.27	81.05
Traffic light	70.34	72.27	Train	71.55	76.39
Traffic sign	70.23	73.48	Mocycle	64.58	68.78
Vegetation	90.07	92.19	Bicycle	75.09	78.59
Terrain	68.71	63.27	MIoU	74.13	76.27

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Na CHEN, Rong-fen ZHANG, Yu-hong LIU, Li LI, Wen-wen ZHANG. Semantic segmentation algorithm based on class feature attention mechanism fusion[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(2): 236

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