面向域自适应目标检测的一致无偏教师模型

doi:10.11996/JG.j.2095-302X.2025010114

图学学报 ›› 2025, Vol. 46 ›› Issue (1): 114-125.DOI: 10.11996/JG.j.2095-302X.2025010114

• 图像处理与计算机视觉 • 上一篇下一篇

面向域自适应目标检测的一致无偏教师模型

程旭东¹^,²(), 史彩娟¹^,²(), 高炜翔¹^,², 王森¹^,², 段昌钰¹^,², 闫晓东¹^,²

1.华北理工大学人工智能学院，河北唐山 063210
2.河北省工业智能感知重点实验室，河北唐山 063210

收稿日期:2024-07-01 接受日期:2024-10-19 出版日期:2025-02-28 发布日期:2025-02-14
通讯作者:史彩娟(1977-)，女，教授，博士。主要研究方向为计算机视觉、图像处理和深度学习。E-mail：scj-blue@163.com
第一作者:程旭东(1999-)，男，硕士研究生。主要研究方向为迁移学习和目标检测。E-mail：chengxd99@163.com
基金资助:
北京市现代信息科学与网络技术重点实验室开放课题(XDXX2301);华北理工大学杰出青年基金(JQ201715);唐山市人才项目(A202110011)

Consistent and unbiased teacher model research for domain adaptive object detection

CHENG Xudong¹^,²(), SHI Caijuan¹^,²(), GAO Weixiang¹^,², WANG Sen¹^,², DUAN Changyu¹^,², YAN Xiaodong¹^,²

1. College of Artificial Intelligence, North China University of Science and Technology, Tangshan Hebei 063210, China
2. Hebei Key Laboratory of Industrial Intelligent Perception, Tangshan Hebei 063210, China

Received:2024-07-01 Accepted:2024-10-19 Published:2025-02-28 Online:2025-02-14
Contact: SHI Caijuan (1977-), professor, Ph.D. Her main research interests cover computer vision, image processing and deep learning. E-mail：scj-blue@163.com
First author：CHENG Xudong (1999-), master student. His main research interests cover transfer learning and object detection. E-mail：chengxd99@163.com
Supported by:
Open project of Beijing Key Laboratory of Modern Information Science and Network Technology(XDXX2301);Distinguished Youth Foundation of North China University of Science and Technology(JQ201715);Talent Foundation of Tangshan(A202110011)

摘要/Abstract

摘要：

作为一种重要手段，自训练方法极大提升了域自适应目标检测(DAOD)性能，其主要通过教师网络对目标域数据进行预测，然后选择高置信度的预测结果作为伪标签来指导学生网络训练。然而，由于源域与目标域存在显著的域差异，教师网络产生的伪标签质量不佳，进而影响学生网络训练，降低了模型性能。因此，提出一种面向DAOD的一致无偏教师(CUT)模型。首先，在教师网络设计自适应阈值生成(ATG)模块，该模块通过高斯混合模型(GMM)在训练过程为每张图像生成自适应阈值筛选伪标签，保证伪标签数量时序一致性，提高伪标签质量。其次，提出预测引导样本选择(PSS)策略，借助教师网络中区域建议网络的预测结果为学生网络选择样本，使选择的样本与真实结果具有一致性，降低质量不佳伪标签对学生网络的影响。此外，为了提升对小目标和数量较少困难类别目标的检测性能，设计混合域增强(MDA)模块，在训练过程中生成包含源域和类目标域随机信息的混合域图像对学生网络进行训练。将该模型在3个场景数据集进行实验，性能分别提升4.0%，5.8%和3.7%，验证了该算法的有效性。值得注意的是，该模型CUT首次利用自训练方法来解决可见光图像到红外图像的较大域差异问题。

关键词: 域自适应, 目标检测, 自训练, 伪标签, 一致性

Abstract:

As a significant approach, the self-training method has significantly enhanced the performance of domain adaptive object detection methods. The self-training method primarily predicts target domain data through a teacher network, and then selects high-confidence predictions as pseudo-labels to guide student network training. However, due to significant domain differences between the source and target domains, the pseudo-labels generated by the teacher network are often of poor quality, adversely impacting student network training and reducing the performance of object detection. To address this challenge, a consistent and unbiased teacher (CUT) model for domain adaptive object detection was proposed. Firstly, an adaptive threshold generation (ATG) module was designed within the teacher network. The ATG module utilized a Gaussian mixture model (GMM) during training to generate adaptive thresholds for each image, ensuring temporal consistency of pseudo-label quantities and thereby enhancing their quality. Secondly, a prediction-guided sample selection (PSS) strategy was introduced, which leveraged predictions from the region proposal network within the teacher network to guide the selection of positive and negative samples for the student network. The PSS strategy effectively aligned the selected samples with real outcomes, thereby mitigating the impact of low-quality pseudo-labels on the student network. Furthermore, to improve detection performance for small objects and challenging objects with fewer instances, a mixed domain augmentation (MDA) module was devised to generate mixed-domain images containing random information from both the source and target-like domains to supervise student network training. Extensive experiments conducted on three scenario datasets demonstrated the effectiveness of the proposed CUT, with performance improvements of 4.0%, 5.8%, and 3.7%, respectively. Notably, the proposed CUT model applied the self-training method for the first time to address the problem of large domain disparities between visual images and infrared images.

Key words: domain adaption, object detection, self-training, pseudo-labels, consistency

中图分类号:

程旭东, 史彩娟, 高炜翔, 王森, 段昌钰, 闫晓东. 面向域自适应目标检测的一致无偏教师模型[J]. 图学学报, 2025, 46(1): 114-125.

CHENG Xudong, SHI Caijuan, GAO Weixiang, WANG Sen, DUAN Changyu, YAN Xiaodong. Consistent and unbiased teacher model research for domain adaptive object detection[J]. Journal of Graphics, 2025, 46(1): 114-125.

图/表 18

图1 固定阈值下伪标签数量的时序变化趋势

Fig. 1 Temporal trend of the number of pseudo-labels under two fixed thresholds

图2 定位不准伪标签导致样本选择错误的示例

Fig. 2 An example of incorrect sample selection due to a pseudo-label with inaccurate positioning

图3 一致无偏教师CUT模型结构图

Fig. 3 The framework of consistent and unbiased teacher (CUT) model

图4 自适应阈值生成模块ATG

Fig. 4 Adaptive threshold generation (ATG) module

图5 预测引导样本选择策略PSS

Fig. 5 Prediction-guided sample selsetion (PSS) strategy

图6 学生网络建议框质量q计算过程

Fig. 6 The calculation process of student network anchors quality q

图7 混合域生成模块MDA

Fig. 7 Mixed domain augmentation (MDA) module

图8 CycleGAN风格迁移数据集((a)正常天气图像到复杂天气图像；(b)可见图像到红外图像；(c)合成图像到真实图像)

Fig. 8 CycleGAN image-to-image translation datasets ((a) Normal weather images to complex weather images; (b) Visual images to infrared images; (c) Synthesize images to real images)

表1 Cityscapes到Foggy cityscapes实验结果/%

Table 1 Experimental results from Cityscapes to Foggy cityscapes/%

模型	模型来源	Person	Rider	Car	Truck	Bus	Train	Mcycle	Bicycle	mAP
EPM^[16]	ECCV'20	41.9	38.7	56.7	22.6	41.5	26.8	24.6	35.5	36.0
HTCN^[32]	CVPR'20	33.2	47.5	47.9	31.6	47.4	40.9	32.3	37.1	39.8
MeGA-CDA^[33]	CVPR'21	37.7	49.0	52.4	25.4	49.2	46.9	34.5	39.0	41.8
TIA^[13]	CVPR'22	34.8	46.3	49.7	31.1	52.1	48.6	37.7	38.1	42.3
SCAN^[34]	AAAI'22	41.7	43.9	57.3	28.7	48.6	48.7	31.0	37.3	42.1
SIGMA^[20]	CVPR'22	44.0	43.9	60.3	31.6	50.4	51.5	31.7	40.6	44.2
PT^[24]	ICML'22	40.2	48.8	59.7	30.7	51.8	30.6	35.4	44.5	42.7
LRA^[35]	TNNLS'23	45.6	47.1	59.7	32.1	52.4	44.6	34.8	39.9	44.5
DA-Detect^[36]	WACV'23	36.5	46.7	54.3	30.3	51.2	48.7	31.6	39.1	42.3
PT+CMT^[26]	CVPR'23	42.3	51.7	64.0	26.0	42.7	37.1	42.5	44.0	43.8
CIGAR^[37]	CVPR'23	45.3	45.3	61.6	32.1	50.0	51.0	31.9	40.4	44.7
MCNet^[38]	PR'24	48.0	50.4	62.4	26.5	46.3	34.3	37.1	43.1	43.5
DSCA^[39]	PR'24	40.5	44.3	60.2	29.3	50.1	49.5	30.6	37.6	42.7
CRADA^[40]	TMM'24	47.0	48.3	64.1	28.7	52.3	38.6	35.1	41.5	44.5
UGA^[19]	TOMM'24	39.5	51.1	57.5	35.0	51.4	44.2	37.3	43.6	45.0
Baseline^[22]	CVPR'21	33.0	46.7	48.6	34.1	56.5	46.8	30.4	37.3	41.7
CUT(本文)	-	41.1	52.0	57.3	33.6	52.2	45.7	39.4	44.0	45.7

表2 RGB FLIR到Thermal FLIR实验结果/%

Table 2 Experimental results from RGB FLIR to Thermal FLIR/%

模型	模型来源	Car	Bicycle	Person	mAP
SWDA^[14]	CVPR'19	58.9	32.0	32.3	41.4
CR-DA-DET^[41]	CVPR'20	58.3	37.6	46.3	47.4
HTCN^[32]	CVPR'20	56.3	37.9	33.1	42.4
EPM^[16]	ECCV'20	53.8	39.0	41.0	44.6
VDD^[42]	ICCV'21	66.6	49.2	45.8	53.9
TIA^[13]	CVPR'22	67.8	44.8	48.8	53.8
UDAT^[30]	TVC'23	66.8	49.3	43.4	53.2
UGA^[19]	TOMM'24	68.5	48.0	51.6	56.0
Baseline^[22]	CVPR'21	65.3	43.5	49.5	52.8
CUT(本文)	-	68.3	52.7	54.9	58.6

表3 SIM10K到Cityscapes实验结果

Table 3 Experimental results from SIM10K to Cityscapes

模型	模型来源	AP on Car/%
SCDA^[15]	CVPR'19	43.0
EPM^[16]	ECCV'20	43.2
HTCN^[32]	CVPR'20	42.5
SED^[43]	AAAI'21	42.5
MeGA-CDA^[33]	CVPR'21	44.8
SCAN^[34]	AAAI'22	45.8
SIGMA^[20]	CVPR'22	45.8
Baseline^[22]	CVPR'21	43.1
CUT(本文)	-	46.8

图9 视觉比较

Fig. 9 Vision comparison ((a) Baseline; (b) TIA; (c) CUT; (d) Groud truth)

表4 消融研究/%

Table 4 Ablation study/%

模型	ATG	PSS	MDA	mAP(C→F)	mAP(R→T)	AP on car(S→C)
Baseline	-	-	-	41.7	52.8	43.1
#1	√	-	-	43.1	54.3	44.7
#2	-	√	-	43.8	53.3	44.5
#3	-	-	√	44.2	55.0	44.6
#4	√	√	-	44.0	54.4	45.6
#5	√	-	√	45.4	57.0	46.1
#6	-	√	√	45.0	55.4	45.7
CUT(本文)	√	√	√	45.7	58.6	46.8

图10 不同策略下伪标签数量变化趋势

Fig. 10 Variation trend of the number of pseudo-labels under different strategies

图11 不同数据集下阈值变化趋势

Fig. 11 Variation trend of thresholds under different datasets

图12 MDA模块对小目标检测性能的影响

Fig. 12 The impact of MDA module on the detection performance of small objects ((a) Baseline; (b) With MDA)

图13 MDA模块对困难类别目标检测性能的影响

Fig. 13 The impact of MDA module on the detection performance of difficult category objects

表5 超参数α分析

Table 5 Analysis of hyperparameter α

质量q			α
质量q	0	0.2	0.5	0.8	1.0
mAP/%	41.7	43.8	43.2	42.9	41.2

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面向域自适应目标检测的一致无偏教师模型

Consistent and unbiased teacher model research for domain adaptive object detection

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