High-low dimensional feature guided real-time semantic segmentation network

doi:10.11772/j.issn.1001-9081.2022091438

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (10): 3077-3085.DOI: 10.11772/j.issn.1001-9081.2022091438

Special Issue: 人工智能

• Artificial intelligence • Previous Articles Next Articles

High-low dimensional feature guided real-time semantic segmentation network

Zixing YU¹, Shaojun QU¹(), Xin HE², Zhuo WANG¹

^1.College of Information Science and Engineering，Hunan Normal University，Changsha Hunan 410081，China
^2.Hunan Novasky Electronic Technology Company Limited，Changsha Hunan 410221，China

Received:2022-09-29 Revised:2022-12-06 Accepted:2022-12-12 Online:2023-03-23 Published:2023-10-10
Contact: Shaojun QU
About author:YU Zixing， born in 1997， M. S. candidate. His research interests include computer vision， deep learning.
QU Shaojun， born in 1979， Ph. D.， senior experimentalist. His research interests include image segmentation， computer vision， deep learning.
HE Xin， born in 1987， Ph. D. His research interests include deep learning， radar-vision fusion.
WANG Zhuo， born in 2000， M. S. candidate. Her researchinterests include computer vision， deep learning.
Supported by:
National Natural Science Foundation of China(12071126)

高低维特征引导的实时语义分割网络

虞资兴¹, 瞿绍军¹(), 何鑫², 王卓¹

^1.湖南师范大学信息科学与工程学院，长沙 410081
^2.湖南华诺星空电子技术有限公司，长沙 410221

通讯作者: 瞿绍军
作者简介:虞资兴（1997—），男，湖南株洲人，硕士研究生，CCF会员，主要研究方向：计算机视觉、深度学习
瞿绍军（1979—），男，湖南永顺人，正高级实验师，博士，CCF会员，主要研究方向：图像分割、计算机视觉、深度学习. qshj@hunnu. edu. cn
何鑫（1987—），男，湖南邵阳人，博士，主要研究方向：深度学习、雷达视觉融合
王卓（2000—），女，湖南邵阳人，硕士研究生，CCF会员，主要研究方向：计算机视觉、深度学习。
基金资助:
国家自然科学基金资助项目(12071126)

Abstract

Abstract:

Most semantic segmentation networks use bilinear interpolation to restore the resolution of the high-level feature map to the same resolution as the low-level feature map and then perform fusion operation， which causes that part of high-level semantic information cannot be spatially aligned with the low-level feature map， resulting in the loss of semantic information. To solve the problem， based on the improvement of Bilateral Segmentation Network （BiSeNet）， a High-Low dimensional Feature Guided real-time semantic segmentation Network （HLFGNet） was proposed. First， High-Low dimensional Feature Guided Module （HLFGM） was proposed to guide the displacement of high-level semantic information during the upsampling process through the spatial position information of the low-level feature map. At the same time， the strong feature representations were obtained by the high-level feature maps， and by combining with the attention mechanism， the redundant edge detail information in the low-level feature map was eliminated and the pixel misclassification was reduced. Then， the improved Pyramid Pooling Guided Module （PPGM） was introduced to obtain global contextual information and strengthen the effective fusion of local contextual information at different scales. Experimental results on Cityscapes validation set and CamVid test set show that HLFGNet has the mean Intersection over Union （mIoU） of 76.67% and 70.90% respectively， the frames per second reached 75.0 and 96.2 respectively. In comparison with BiSeNet， HLFGNet has the mIoU increased by 1.76 and 3.40 percentage points respectively. It can be seen that HLFGNet can accurately identify the scene information and meet the real-time requirements.

Key words: real-time semantic segmentation, upsampling, attention mechanism, pyramid pooling, contextual information

摘要：

多数语义分割网络利用双线性插值将高级特征图的分辨率恢复至与低级特征图一样的分辨率再进行融合操作，导致部分高级语义信息在空间上无法与低级特征图对齐，进而造成语义信息的丢失。针对以上问题，改进双边分割网络（BiSeNet），并基于此提出一种高低维特征引导的实时语义分割网络（HLFGNet）。首先，提出高低维特征引导模块（HLFGM）来通过低级特征图的空间位置信息引导高级语义信息在上采样过程中的位移；同时，利用高级特征图来获取强特征表达，并结合注意力机制来消除低级特征图中冗余的边缘细节信息以及减少像素误分类的情况。其次，引入改进后的金字塔池化引导模块（PPGM）来获取全局上下文信息并加强不同尺度局部上下文信息的有效融合。在Cityscapes验证集和CamVid测试集上的实验结果表明，HLFGNet的平均交并比（mIoU）分别为76.67%与70.90%，每秒传输帧数分别为75.0、96.2；而相较于BiSeNet，HLFGNet的mIoU分别提高了1.76和3.40个百分点。可见，HLFGNet能够较为准确地识别场景信息，并能满足实时性要求。

关键词: 实时语义分割, 上采样, 注意力机制, 金字塔池化, 上下文信息

CLC Number:

TP391.4

Zixing YU, Shaojun QU, Xin HE, Zhuo WANG. High-low dimensional feature guided real-time semantic segmentation network[J]. Journal of Computer Applications, 2023, 43(10): 3077-3085.

虞资兴, 瞿绍军, 何鑫, 王卓. 高低维特征引导的实时语义分割网络[J]. 《计算机应用》唯一官方网站, 2023, 43(10): 3077-3085.

Figures/Tables 13

Fig. 1 High-low dimensional feature guided real-time semantic segmentation network

Tab. 1 Detail structure of ResNet-18

名称	操作	输出尺寸
输入	18-layer	1 024×1 024
Conv	$7 × 7$ ，64，stride=2	512×512
Pooling	$3 × 3$ ，Maxpool，stride=2	256×256
Res1	$3 × 3,64 3 × 3,64 × 2$	256×256
Res2	$3 × 3,128 3 × 3,128 × 2$	128×128
Res3	$3 × 3,256 3 × 3,256 × 2$	64×64
Res4	$3 × 3,512 3 × 3,512 × 2$	32×32

Tab. 1 Detail structure of ResNet-18

名称	操作	输出尺寸
输入	18-layer	1 024×1 024
Conv	$7 × 7$ ，64，stride=2	512×512
Pooling	$3 × 3$ ，Maxpool，stride=2	256×256
Res1	$3 × 3,64 3 × 3,64 × 2$	256×256
Res2	$3 × 3,128 3 × 3,128 × 2$	128×128
Res3	$3 × 3,256 3 × 3,256 × 2$	64×64
Res4	$3 × 3,512 3 × 3,512 × 2$	32×32

Tab. 2 Detail structure of detail branch

输入	操作	卷积核大小	输出通道大小	步长	输出尺寸
Stage1	Conv2d	7	64	2	512×512
Stage2	Conv2d	3	64	2	256×256
Stage3	Conv2d	3	64	2	128×128
Stage4	Conv2d	1	128	1	128×128

Fig. 2 High-low dimensional feature guided module

Fig. 3 Visual comparison of high-level feature map

Fig. 4 Pyramid pooling guided module

Tab. 3 Performance validation on Cityscapes validation set under different settings

分割模型	UP	HLFGM	AVG	PPM	PPGM	参数量/MB	mIoU/%	帧率/（frame·s^-1）
BiSeNet	√		√			51.35	74.91	83.0
	√			√		50.28	75.21	79.0
	√				√	50.35	76.25	76.0
HLFGNet		√	√			51.14	75.10	71.0
		√		√		50.29	76.01	78.0
		√			√	50.53	76.67	75.0

Tab. 4 Weighting coefficient experiment results

α	mIoU/%	α	mIoU/%
1.0	75.82	0.5	76.51
0.9	75.64	0.4	76.42
0.8	76.40	0.3	76.29
0.7	76.67	0.0	63.80
0.6	76.52

Tab. 5 Comparison experiment results of Guide operation on feature maps with different scales

模块	1×1	2×2	3×3	6×6	参数量/MB	mIoU/%	帧率/（frame·s^-1）
PPGM-1	√	√	√	√	50.53	76.09	68.0
PPGM-2		√	√	√	50.53	76.88	70.0
PPGM-3			√	√	50.53	76.67	75.0
PPGM-4				√	50.53	75.51	76.0
PPM					50.29	76.01	78.0

Tab. 6 Comparison of experimental results of different networks on Cityscapes dataset

网络	输入尺寸	骨干网络	参数量/MB	mIoU/%		帧率/（frame·s^-1）
网络	输入尺寸	骨干网络	参数量/MB	验证集	测试集	帧率/（frame·s^-1）
ICNet	1 024×2 048	PSPNet50	28.30	71.70	69.50	15.2
DFANet-A	1 024×1 024	Xception	7.80	71.90	71.30	50.0
FasterSeg	1 024×2 048	无	4.40	73.10	71.50	94.2
DF2-Seg2	1 024×2 048	DF2	56.55	76.90	75.30	28.2
STDC2-Seg75	768×1 536	STDC	61.67	77.00	76.80	48.9
STDC2-Seg75*	768×1 536	STDC	61.67	74.20	73.20	48.9
BiSeNet	1 024×2 048	ResNet18	51.35	74.91	74.50	83.0
BiSeNet V2	1 024×2 048	无	20.16	74.20	72.90	83.4
BASeNet	1 024×2 048	无	—	77.50	74.90	47.2
BiSeNet V2-L	512×1 024	无	123.75	75.80	75.30	23.6
HLFGNet	1 024×2 048	ResNet18	50.53	76.67	75.40	75.0

Tab. 7 Accuracy for each category on Cityscapes test set

类别	FasterSeg	HLFGNet	BiSeNet
平均值	71.45	75.43	74.56
road	98.02	98.27	98.20
sidewalk	83.48	83.74	83.17
building	91.13	91.82	91.62
wall	39.13	45.19	44.99
fence	48.70	52.77	50.69
pole	58.60	61.53	61.99
traffic light	66.73	71.04	71.29
traffic sign	71.60	74.21	74.63
vegetation	92.33	92.81	92.78
terrain	69.11	71.51	70.44
sky	94.49	95.19	94.91
person	81.47	83.63	83.40
rider	61.78	66.09	66.19
car	93.68	95.25	94.98
truck	54.95	64.53	61.38
bus	67.09	77.86	75.45
train	61.11	72.14	67.03
motorcycle	54.90	63.05	61.22
bicycle	69.21	72.58	72.34

Tab. 8 Comparison and analysis of different models on CamVid test set

模型	输入尺寸	mIoU/%	帧率/（frame·s^-1）
SegNet	720×960	60.10	4.6
ICNet	720×960	67.10	34.5
ENet	720×960	51.30	61.2
BiSeNet V2	720×960	70.80	81.9
BiSeNet	720×960	67.50	115.0
HLFGNet	720×960	70.90	96.2

Fig. 5 Visual comparison of semantic segmentation experimental effects

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High-low dimensional feature guided real-time semantic segmentation network

高低维特征引导的实时语义分割网络

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