Design of Swin Transformer for semantic segmentation of road scenes

Table 1. Experimental environment
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Table 1. Experimental environment
实验环境配置实验环境配置
CPU AMD5600Xd CPU核心数 6
GPU NVIDIA RTX3070 主频 3.7 GHz
内存 32 G 显存 11 G
操作系统 Ubuntu18.04 编程语言 Python 3.7
深度学习框架 Pytorch 1.10.0 CUDA 10.2

Table 2. IoU and MIoU of various models on the Cityscapes dataset

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Table 2. IoU and MIoU of various models on the Cityscapes dataset

Classes	FCN	PSPNet	UNet	DeepLabv3	SwinT	Ours
Road	97.1	98.0	98.0	98.1	98.0	98.1
Sidewalk	79.9	81.8	84.2	84.5	84.7	86.2
Building	89.3	91.1	91.1	91.7	91.4	91.6
Wall	44.2	48.2	48.7	51.2	54.4	55.5
Fence	48.3	50.3	51.5	53.6	57.3	59.9
Pole	30.6	45.7	48.2	50.3	55.5	57.2
Traffic Light	44.7	50.0	51.7	53.7	61.9	63.2
Traffic Sign	56.8	62.3	65.8	68.2	73.5	74.4
Vegetation	87.1	89.2	90.1	90.1	90.2	92.4
Terrain	60.4	62.8	65.3	64.2	61.3	63.2
Sky	90.8	94.2	93.8	95.3	94.2	95.1
Person	64.1	71.2	72.6	74.5	75.5	76.9
Rider	38.2	45.6	46.1	49.5	55.7	55.9
Car	90.4	92.0	92.2	92.6	93.8	93.5
Truck	51.3	68.5	63.4	74.4	73.6	72.5
Bus	72.0	80.3	77.6	83.2	79.4	79.9
Train	74.4	77.4	78.5	81.5	77.7	78.1
Motocycle	52.5	50.1	55.5	53.5	56.5	59.2
Bicycle	59.1	60.1	63.4	64.2	71.2	73.2
MIoU/%	64.92	69.28	70.45	73.71	73.17	75.18

Table 3. PA and MPA of various models on the Cityscapes dataset

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Table 3. PA and MPA of various models on the Cityscapes dataset

Classes	FCN	PSPNet	UNet	DeepLabv3	SwinT	Ours
Road	98.1	98.5	98.8	99.1	99.1	99.1
Sidewalk	89.9	89.3	90.2	92.0	91.2	92.7
Building	96.3	94.7	96.1	96.2	96.5	96.8
Wall	52.2	72.1	60.7	73.1	71.4	72.3
Fence	60.3	69.3	68.5	72.5	71.4	74.6
Pole	36.6	74.7	59.2	74.3	74.1	77.7
Traffic Light	56.7	72.0	62.7	69.2	70.4	72.1
Traffic Sign	68.8	79.3	75.8	76.5	76.7	79.3
Vegetation	94.1	93.2	95.1	93.6	95.3	97.7
Terrain	74.4	79.8	78.3	78.1	79.2	80.3
Sky	95.8	97.2	97.8	97.5	97.5	97.9
Person	77.1	82.2	84.6	84.2	86.3	87.9
Rider	58.2	68.6	55.1	71.2	72.4	73.7
Car	96.4	96.0	96.2	96.3	97.6	97.6
Truck	62.3	79.5	76.4	75.5	73.5	76.2
Bus	85.0	87.3	89.6	91.7	85.6	87.7
Train	78.4	83.4	92.5	88.4	79.3	82.9
Motocycle	66.5	73.5	67.5	77.5	77.3	79.2
Bicycle	77.1	73.1	80.4	76.2	80.3	84.2
MPA/%	74.64	79.97	80.06	82.31	81.59	84.83

Table 4. Performance comparison of various semantic segmentation algorithms

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Table 4. Performance comparison of various semantic segmentation algorithms

方法	MIoU/%	MPA/%	Param/M	FLOPs/G	FPS
FCN	64.92	74.64	34.90	66.38	58.61
PSPNet	69.28	79.97	51.86	152.97	81.25
UNet	70.45	80.06	49.10	166.92	54.52
DeepLabv3	73.71	82.31	68.37	235.37	36.59
SwinT	73.17	81.59	121.25	297.57	12.22
Ours	75.18	84.83	123.77	305.46	14.83

Table 5. Ablation experiment
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Table 5. Ablation experiment
实验序号 AFM FCM ASPP MIoU/% MPA/%
注：“√”表示网络中包含该结构，“×”表示在网络中去掉该结构。
① × × × 73.1 81.6
② √ × × 73.8 80.5
③ √ √ × 74.9 83.3
④ √ √ √ 75.2 84.8

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Hao Hang, Yingping Huang, Xurui Zhang, Xin Luo. Design of Swin Transformer for semantic segmentation of road scenes[J]. Opto-Electronic Engineering, 2024, 51(1): 230304-1

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