基于掩码增强自训练的域适应语义分割

doi:10.11772/j.issn.1001-9081.2024070935

《计算机应用》唯一官方网站 ›› 2025, Vol. 45 ›› Issue (7): 2132-2137.DOI: 10.11772/j.issn.1001-9081.2024070935

• CCF第39届中国计算机应用大会 (CCF NCCA 2024) • 上一篇下一篇

基于掩码增强自训练的域适应语义分割

冯博¹, 于海征¹(), 边红²

^1.新疆大学数学与系统科学学院，乌鲁木齐 830017
^2.新疆师范大学数学科学学院，乌鲁木齐 830017

收稿日期:2024-07-05 修回日期:2024-10-12 接受日期:2024-10-16 发布日期:2025-07-10 出版日期:2025-07-10
通讯作者: 于海征
作者简介:冯博（1999—），男，山西运城人，硕士研究生，主要研究方向：大数据分析
边红（1974—）女，甘肃灵台人，教授，博士，主要研究方向：网络优化、图神经网络。
基金资助:
国家自然科学基金资助项目(12361072);新疆维吾尔自治区自然科学基金资助项目(2023D01A36);新疆维吾尔自治区自然科学基金资助项目(2023D01B48)

Domain adaptive semantic segmentation based on masking enhanced self-training

Bo FENG¹, Haizheng YU¹(), Hong BIAN²

^1.College of Mathematics and System Science，Xinjiang University，Urumqi Xinjiang 830017，China
^2.School of Mathematical Sciences，Xinjiang Normal University，Urumqi Xinjiang 830017，China

Received:2024-07-05 Revised:2024-10-12 Accepted:2024-10-16 Online:2025-07-10 Published:2025-07-10
Contact: Haizheng YU
About author:FENG Bo， born in 1999， M. S. candidate. His research interests include big data analytics.
BIAN Hong， born in 1974， Ph. D.， professor. Her research interests include network optimization， graph neural networks.
Supported by:
National Natural Science Foundation of China(12361072);Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023D01A36)

摘要/Abstract

摘要：

近年来，基于卷积神经网络（CNN）的语义分割模型在多种应用中表现出了卓越的性能。然而，这些模型在应用于新领域时通常不能很好地泛化，特别是从合成数据应用到真实数据的情况。无监督域适应（UDA）问题旨在尝试在一个带有标记数据的已知领域（源域）上进行模型训练，同时在无标记数据的未知领域（目标域）中学习，以提升源域训练的分割模型在目标域的泛化能力。现有方法虽然通过用自训练对无标记目标域图像进行伪标签训练的方式取得了很大进展，并提出多种方式来减少因领域迁移而产生的低质量伪标签，但效果参差不齐。针对该问题，提出一种基于掩码增强自训练的域适应方法对目标域图像掩码增强处理以生成伪标签，并对未掩码目标图像生成的伪标签进行修正，并且以最小化掩码图像伪标签和未掩码图像修正伪标签的一致性损失为目标，使模型学习到更多目标域特征的同时生成更鲁棒的伪标签。实验结果表明，所提方法在GTA5 （Grand Theft AutoV）→Cityscapes和SYNTHIA（SYNTHetic collection of Imagery and Annotations）→Cityscapes两项UDA任务常用的语义分割基准实验中均取得了不错的性能，比经典的DACS（Domain Adaptation Cross-domain Sampling）方法在GTA5和SYNTHIA数据集上的平均交并比（mIoU）分别提高了1.3和1.2个百分点；另外，消融实验结果也验证了所提掩码增强及伪标签修正模块的有效性。可见，所提出的自训练域适应方法学习到了更多的目标域上下文信息，并且提升了分割模型在目标域的泛化能力。

关键词: 掩码增强, 自训练机制, 语义分割, 迁移学习, 域适应

Abstract:

In recent years， semantic segmentation models based on Convolutional Neural Network （CNN） have shown excellent performance in a variety of applications. However， these models usually do not generalize well when they are applied to new domains， especially from synthetic to real data. The problem of Unsupervised Domain Adaptation （UDA） is attempting to train in a known domain with labeled data （the source domain） while learning in an unknown domain with unlabeled data （the target domain）， in order to improve the generalization ability of the segmentation model trained in the source domain to the target domain. The existing methods have made great progress through training pseudo-labels on unlabeled target domain images by self-training， and various ways have been proposed to reduce low-quality pseudo-labels brought by domain migration， but the above leads to mixed results. Aiming at this problem， a masking enhanced self-training domain adaptation method was proposed to generate pseudo-labels for target domain image masking enhanced processing and correct pseudo-labels generated from unmasked target images， and with the goal of minimizing loss of consistency between the pseudo-labels of masked image and the corrected pseudo-labels of unmasked image， more features of the target domain were learnt by the model and more robust pseudo-labels were generated by the model at the same time. Experimental results show that the proposed method achieves good performance in benchmark experiments of semantic segmentation used commonly in two UDA tasks， GTA5 （Grand Theft AutoV） → Cityscapes and SYNTHIA （SYNTHetic collection of Imagery and Annotations） → Cityscapes. Specifically， compared with the classical DACS （Domain Adaptation Cross-domain Sampling） method， the proposed method improves the mean Intersection over Union （mIoU） by 1.3 percentage points on the GTA5 dataset， and 1.2 percentage points on SYNTHIA dataset. In addition， the ablation experimental results show the effectiveness of the proposed mask enhancement and pseudo-label correction modules. It can be seen that the proposed self-training domain adaptation method learns more target domain context information and improves generalization ability of the segmentation model in target domain.

Key words: mask enhancement, self-training mechanism, semantic segmentation, transfer learning, domain adaptation

中图分类号:

TP391

冯博, 于海征, 边红. 基于掩码增强自训练的域适应语义分割[J]. 计算机应用, 2025, 45(7): 2132-2137.

Bo FENG, Haizheng YU, Hong BIAN. Domain adaptive semantic segmentation based on masking enhanced self-training[J]. Journal of Computer Applications, 2025, 45(7): 2132-2137.

图/表 5

参考文献 28

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类别	IoU/%
类别	Source only	ProDA	DACS	本文方法
mIoU	42.3	49.5	50.5	51.8
road	71.3	78.9	80.1	80.5
sidewalk	26.1	32.7	36.7	37.4
building	71.9	78.3	78.2	78.3
wall	27.7	33.4	33.1	35.4
fence	35.7	40.3	38.3	41.1
pole	19.2	24.9	36.1	38.3
light	54.9	60.3	61.0	63.5
sign	33.5	39.5	40.3	42.0
veg	73.4	80.1	79.5	78.1
terrain	49.7	55.5	58.3	60.1
sky	63.3	70.2	72.0	73.4
person	43.6	50.7	54.4	56.5
rider	45.2	57.0	55.2	54.7
car	45.1	53.3	49.9	50.5
truck	27.2	40.9	39.1	45.8
bus	18.9	23.7	25.5	25.6
train	32.4	41.7	43.1	40.2
mbike	37.1	44.5	43.2	45.1
bicycle	27.5	34.9	36.3	38.0

类别	IoU/%
类别	Source only	ProDA	DACS	本文方法
mIoU	42.3	49.5	50.5	51.8
road	71.3	78.9	80.1	80.5
sidewalk	26.1	32.7	36.7	37.4
building	71.9	78.3	78.2	78.3
wall	27.7	33.4	33.1	35.4
fence	35.7	40.3	38.3	41.1
pole	19.2	24.9	36.1	38.3
light	54.9	60.3	61.0	63.5
sign	33.5	39.5	40.3	42.0
veg	73.4	80.1	79.5	78.1
terrain	49.7	55.5	58.3	60.1
sky	63.3	70.2	72.0	73.4
person	43.6	50.7	54.4	56.5
rider	45.2	57.0	55.2	54.7
car	45.1	53.3	49.9	50.5
truck	27.2	40.9	39.1	45.8
bus	18.9	23.7	25.5	25.6
train	32.4	41.7	43.1	40.2
mbike	37.1	44.5	43.2	45.1
bicycle	27.5	34.9	36.3	38.0

类别	IoU/%
类别	Source only	ProDA	DACS	本文方法
mIoU	37.4	48.5	49.1	50.3
road	46.5	78.5	73.1	75.0
sidewalk	14.1	31.1	29.3	32.3
building	65.2	69.9	72.7	72.3
light	27.4	35.9	38.4	40.1
sign	32.5	36.7	36.5	41.1
veg	64.9	69.8	73.2	72.7
sky	58.7	63.2	75.1	74.2
person	43.9	50.3	49.4	48.6
rider	8.4	14.7	12.3	13.6
car	31.0	70.2	62.3	64.9
bus	14.4	19.7	19.2	23.3
mbike	33.7	37.8	42.5	40.2
bicycle	45.7	52.8	53.9	55.1

类别	IoU/%
类别	Source only	ProDA	DACS	本文方法
mIoU	37.4	48.5	49.1	50.3
road	46.5	78.5	73.1	75.0
sidewalk	14.1	31.1	29.3	32.3
building	65.2	69.9	72.7	72.3
light	27.4	35.9	38.4	40.1
sign	32.5	36.7	36.5	41.1
veg	64.9	69.8	73.2	72.7
sky	58.7	63.2	75.1	74.2
person	43.9	50.3	49.4	48.6
rider	8.4	14.7	12.3	13.6
car	31.0	70.2	62.3	64.9
bus	14.4	19.7	19.2	23.3
mbike	33.7	37.8	42.5	40.2
bicycle	45.7	52.8	53.9	55.1

方法	掩码增强模块	伪标签修正模块	mIoU/%	mIoU增益
参照组			42.3	—
方法1	√		49.7	+7.4
方法2		√	50.6	+8.3
方法3	√	√	51.8	+9.5

基于掩码增强自训练的域适应语义分割

Domain adaptive semantic segmentation based on masking enhanced self-training

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