基于注意力机制和循环域三元损失的域自适应目标检测

周洋; 韩冰; 高新波; 杨铮; 陈玮铭

doi:10.16383/j.aas.c220938

基于注意力机制和循环域三元损失的域自适应目标检测

doi: 10.16383/j.aas.c220938 cstr: 32138.14.j.aas.c220938

周洋^1,,
韩冰^1,,
高新波^{1, 2,},
杨铮^1,,
陈玮铭^1,

1.
西安电子科技大学电子工程学院西安 710071
2.
重庆邮电大学图像认知重庆市重点实验室重庆 400065

基金项目: 国家自然科学基金(62076190, 41831072, 62036007), 陕西省重点创新产业链基金(2022ZDLGY01-11), 西安市重点产业链技术攻关项目(23ZDCYJSGG0022-2023), 国家空间科学数据中心青年开放课题基金(NSSDC2302005)资助

详细信息

作者简介:
周洋：西安电子科技大学电子工程学院硕士研究生. 2020年获得西南石油大学电子信息工程专业学士学位. 主要研究方向为计算机视觉和域自适应目标检测. E-mail: yzhou_6@stu.xidian.edu.cn

韩冰：西安电子科技大学电子工程学院教授. 主要研究方向为智能辅助驾驶系统, 视觉感知与认知, 空间物理与人工智能交叉. 本文通信作者. E-mail: bhan@xidian.edu.cn

高新波：西安电子科技大学教授. 主要研究方向为机器学习, 图像处理, 计算机视觉, 模式识别和多媒体内容分析. E-mail: xbgao@ieee.org

杨铮：西安电子科技大学电子工程学院博士研究生. 2017年获得西安电子科技大学智能科学与技术专业学士学位. 主要研究方向为深度学习, 目标跟踪和强化学习. E-mail: zhengy@stu.xidian.edu.cn

陈玮铭：西安电子科技大学电子工程学院硕士研究生. 2019年获得西安电子科技大学机械设计制造及其自动化专业学士学位. 主要研究方向为计算机视觉, 目标检测和遥感技术. E-mail: wmchen@stu.xidian.edu.cn

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出版历程
- 收稿日期: 2022-12-05
- 录用日期: 2023-05-18
- 网络出版日期: 2023-08-18
- 刊出日期: 2024-11-26

Domain Adaptive Object Detection Based on Attention Mechanism and Cycle Domain Triplet Loss

ZHOU Yang^1
,,
HAN Bing^1
,,
GAO Xin-Bo^{1, 2
,},
YANG Zheng^1
,,
CHEN Wei-Ming^1
,

1.
School of Electronic Engineering, Xidian University, Xi＇an 710071
2.
Chongqing Key Laboratory of Image Cognition, Chongqing University of Posts and Telecommunications, Chongqing 400065

Funds: Supported by National Natural Science Foundation of China (62076190, 41831072, 62036007), Key Industry Innovation Chain of Shaanxi Province (2022ZDLGY01-11), Key Industry Chain Technology Research Project of Xi＇an (23ZDCYJSGG0022-2023), and Youth Open Project of National Space Science Data Center (NSSDC2302005)

More Information

Author Bio:
ZHOU Yang　Master student at the School of Electronic Engineering, Xidian University. He received his bachelor degree in electronic and information engineering from Southwest Petroleum University in 2020. His research interest covers computer vision and domain adaptive detection

HAN Bing　Professor at the School of Electronic Engineering, Xidian University. Her research interest covers intelligent auxiliary drive system, visual perception and cognition, and cross-disciplinary research between space physics and artificial intelligence. Corresponding author of this paper

GAO Xin-Bo　Professor at Xidian University. His research interest covers machine learning, image processing, computer vision, pattern recognition, and multimedia content analysis

YANG Zheng　Ph.D. candidate at the School of Electronic Engineering, Xidian University. He received his bachelor degree in intelligent science and technology from Xidian University in 2017. His research interest covers deep learning, object tracking, and reinforcement learning

CHEN Wei-Ming　Master student at the School of Electronic Engineering, Xidian University. He received his bachelor degree in mechanical design manufacture and automation from Xidian University in 2019. His research interest covers computer vision, object detection, and remote sensing

摘要

摘要: 目前大多数深度学习算法都依赖于大量的标注数据并欠缺一定的泛化能力. 无监督域自适应算法能提取到已标注数据和未标注数据间隐式共同特征, 从而提高算法在未标注数据上的泛化性能. 目前域自适应目标检测算法主要为两阶段目标检测器设计. 针对单阶段检测器中无法直接进行实例级特征对齐导致一定数量域不变特征的缺失, 提出结合通道注意力机制的图像级域分类器加强域不变特征提取. 此外, 对于域自适应目标检测中存在类别特征的错误对齐引起的精度下降问题, 通过原型学习构建类别中心, 设计了一种基于原型的循环域三元损失(Cycle domain triplet loss, CDTL)函数, 从而实现原型引导的精细类别特征对齐. 以单阶段目标检测算法作为检测器, 并在多种域自适应目标检测公共数据集上进行实验. 实验结果证明该方法能有效提升原检测器在目标域的泛化能力, 达到比其他方法更高的检测精度, 并且对于单阶段目标检测网络具有一定的通用性.
- 无监督域自适应 /
- 注意力机制 /
- 循环域三元损失函数 /
- 目标检测
Abstract: Most current deep learning algorithms rely heavily on large amounts of annotated data and exist deficiency in generalization ability. The unsupervised domain adaptation algorithm can extract the common implicit invariant features from the labeled data and unlabeled data, so that the algorithm can achieve good generalization performance on the unlabeled data. At present, domain adaptation object detection algorithms are mainly designed as two-stage object detectors. For the one-stage object detectors, the difficulty of explicit aligning instance-level features leads to the absence of a number of domain invariant features. In this paper, an image-level domain classifier combined with channel attention mechanism is proposed to strengthen domain invariant feature extraction. In addition, to address the issue of reduced accuracy caused by inaccurate alignment of category features in domain adaptive object detection, a prototype based cycle domain triplet loss (CDTL) function was designed to construct category centers through prototype learning, thereby we can achieve precise category feature alignment guided by prototypes. One-stage object detection algorithms are used as detectors, and experiments are conducted on various domain adaptive object detection public datasets. The experimental results show that our method can effectively improve the generalization ability of the original detector on the target domain and achieves higher detection accuracy than other methods. Meanwhile, the experiment on different detector indicate our method is universal for the one-stage object detection network.
- Unsupervised domain adaptation /
- attention mechanism /
- cycle domain triplet loss function /
- object detection

HTML全文

图 1 基于注意力机制和循环域三元损失的域自适应目标检测算法流程

Fig. 1 The pipeline of domain adaptive object detection based on attention mechanism and cycle domain triplet loss

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图 2 循环域自适应三元损失函数原理

Fig. 2 Principle of cycle domain adaptive TripleLoss

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图 3 本文方法在CityScapes→FoggyCityScapes上的主观检测结果

Fig. 3 The subjective results of our method on CityScapes→FoggyCityScapes

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图 4 本文方法在SunnyDay→DuskRainy和SunnyDay→NightRainy上的主观检测结果

Fig. 4 The subjective results of our method on SunnyDay→DuskRainy and SunnyDay→NightRainy

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图 5 本文方法在KITTI→CityScapes和Sim10k→CityScapes上的消融实验结果

Fig. 5 The ablation experimental results of our method on KITTI→CityScapes and Sim10k→CityScapes

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图 6 本文方法在VOC→Clipart1k上的主观结果

Fig. 6 The subjective results of our method on VOC→Clipart1k

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图 7 不同循环迭代训练次数在YOLOv3和YOLOv5s检测器上的结果

Fig. 7 The result of different cycle iterations on YOLOv3 and YOLOv5s

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表 1 不同方法在CityScapes→FoggyCityScapes数据集上的对比实验结果(%)

Table 1 The results of different methods on the CityScapes→FoggyCityScapes dataset (%)

方法	检测器	person	rider	car	truck	bus	motor	bike	train	mAP	mGP
DAF^[10]	Faster R-CNN	25.0	31.0	40.5	22.1	35.3	20.0	27.1	20.2	27.7	38.8
SWDA^[11]	Faster R-CNN	29.9	42.3	43.5	24.5	36.2	30.0	35.3	32.6	34.3	70.0
C2F^[14]	Faster R-CNN	34.0	46.9	52.1	30.8	43.2	34.7	37.4	29.9	38.6	79.1
CAFA^[16]	Faster R-CNN	41.9	38.7	56.7	22.6	41.5	24.6	35.5	26.8	36.0	81.9
ICCR-VDD^[21]	Faster R-CNN	33.4	44.0	51.7	33.9	52.0	34.2	36.8	34.7	40.0	—
MeGA^[20]	Faster R-CNN	37.7	49.0	52.4	25.4	49.2	34.5	39.0	46.9	41.8	91.1
DAYOLO^[28]	YOLOv3	29.5	27.7	46.1	9.1	28.2	12.7	24.8	4.5	36.1	61.0
本文方法(v3)	YOLOv3	34.0	37.2	55.8	31.4	44.4	22.3	30.8	50.7	38.3	83.9
MS-DAYOLO^[31]	YOLOv4	39.6	46.5	56.5	28.9	51.0	27.5	36.0	45.9	41.5	68.6
A-DAYOLO^[32]	YOLOv5	32.8	35.7	51.3	18.8	34.5	11.8	25.6	16.2	28.3	—
S-DAYOLO^[34]	YOLOv5	42.6	42.1	61.9	23.5	40.5	24.4	37.3	39.5	39.0	69.9
本文方法(v5)	YOLOv5s	30.9	37.4	53.3	23.8	39.5	24.2	29.9	35.0	34.3	83.8
注: “—”表示该方法没有进行此实验; (v3)表示检测器为YOLOv3; (v5)表示检测器为YOLOv5s; 加粗数值表示对比实验中的最佳结果.

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表 2 不同方法在SunnyDay→DuskRainy数据集上的对比实验结果(%)

Table 2 The results of different methods on the SunnyDay→DuskRainy dataset (%)

方法	检测器	bus	bike	car	motor	person	rider	truck	mAP	$\Delta{\rm{mAP}}$
DAF^[10]	Faster R-CNN	43.6	27.5	52.3	16.1	28.5	21.7	44.8	33.5	5.2
SWDA^[11]	Faster R-CNN	40.0	22.8	51.4	15.4	26.3	20.3	44.2	31.5	3.2
ICCR-VDD^[21]	Faster R-CNN	47.9	33.2	55.1	26.1	30.5	23.8	48.1	37.8	9.5
本文方法(v3)	YOLOv3	50.1	24.9	70.7	24.2	39.1	19.0	53.2	40.2	7.4
本文方法(v5)	YOLOv5s	46.2	22.1	68.2	16.5	34.8	17.5	50.5	36.5	9.4
注: $\Delta {\rm{mAP}}$表示mAP的涨幅程度.

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表 3 不同方法在SunnyDay→NightRainy数据集上的对比实验结果(%)

Table 3 The results of different methods on the SunnyDay→NightRainy dataset (%)

方法	检测器	bus	bike	car	motor	person	rider	truck	mAP	$\Delta {\rm{mAP}}$
DAF^[10]	Faster R-CNN	23.8	12.0	37.7	0.2	14.9	4.0	29.0	17.4	1.1
SWDA^[11]	Faster R-CNN	24.7	10.0	33.7	0.6	13.5	10.4	29.1	17.4	1.1
ICCR-VDD^[21]	Faster R-CNN	34.8	15.6	38.6	10.5	18.7	17.3	30.6	23.7	7.4
本文方法(v3)	YOLOv3	45.0	8.2	51.1	4.0	20.9	9.6	37.9	25.3	5.1
本文方法(v5)	YOLOv5s	40.7	9.3	45.0	0.6	12.8	9.2	32.5	21.5	4.7

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表 4 KITTI→CityScapes和Sim10k→CityScapes数据集上的对比实验结果(%)

Table 4 The results of different methods on KITTI→CityScapes and Sim10k→CityScapes datasets (%)

方法	KITTI→CityScapes		Sim10k→CityScapes
方法	AP	GP	AP	GP
DAF^[10]	38.5	21.0	39.0	22.5
SWDA^[11]	37.9	19.5	42.3	30.8
C2F^[14]	—	—	43.8	35.3
CAFA^[16]	43.2	32.9	49.0	47.7
MeGA^[20]	43.0	32.4	44.8	37.0
DAYOLO^[28]	54.0	82.2	50.9	39.5
本文方法(v3)	61.1	29.4	60.8	37.1
A-DAYOLO^[32]	37.7	—	44.9	—
S-DAYOLO^[34]	49.3	52.9	—	—
本文方法(v5)	60.0	50.4	60.3	56.3

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表 5 CityScapes→FoggyCityScapes数据集上基于YOLOv3的消融实验结果(%)

Table 5 The results of ablation experiment on CityScapes→FoggyCityScapes dataset based on YOLOv3 (%)

方法	person	rider	car	truck	bus	motor	bike	train	mAP
SO	29.8	35.0	44.7	20.4	32.4	14.8	28.3	21.6	28.4
CADC	34.4	38.0	54.7	24.4	45.0	21.2	32.1	49.1	37.2
CDTL	31.1	38.0	46.7	28.9	34.5	23.4	27.8	13.7	30.5
CADC + CDTL	34.0	37.2	55.8	31.4	44.4	22.3	30.8	50.7	38.3
Oracle	34.9	38.8	55.9	25.3	45.0	22.6	33.4	49.1	40.2

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表 6 CityScapes→FoggyCityScapes数据集上基于YOLOv5s的消融实验结果(%)

Table 6 The results of ablation experiment on CityScapes→FoggyCityScapes dataset based on YOLOv5s (%)

方法	person	rider	car	truck	bus	motor	bike	train	mAP
SO	26.9	33.1	39.9	8.9	21.1	11.3	24.8	4.9	21.4
CADC	32.6	37.1	52.7	26.8	38.1	23.0	38.1	32.6	34.1
CDTL	29.7	36.7	43.2	13.1	25.5	17.1	28.7	13.1	26.2
CADC + CDTL	30.9	37.4	53.3	23.8	39.5	24.2	29.9	35.0	34.3
Oracle	34.8	37.9	57.5	24.4	42.7	23.1	33.2	40.8	36.8

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表 7 SunnyDay→DuskRainy数据集上基于YOLOv3的消融实验结果(%)

Table 7 The results of ablation experiment on SunnyDay→DuskRainy dataset based on YOLOv3 (%)

方法	bus	bike	car	motor	person	rider	truck	mAP
SO	43.7	14.3	68.4	12.0	31.5	10.9	48.7	32.8
CADC	50.0	22.6	70.8	23.2	38.4	18.7	53.5	39.6
CDTL	45.4	20.1	69.2	15.2	34.8	17.2	47.8	35.7
CADC + CDTL	50.1	24.9	70.7	24.2	39.1	19.0	53.2	40.2

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表 8 SunnyDay→DuskRainy数据集上基于YOLOv5s的消融实验结果(%)

Table 8 The results of ablation experiment on SunnyDay→DuskRainy dataset based on YOLOv5s (%)

方法	bus	bike	car	motor	person	rider	truck	mAP
SO	37.2	8.4	63.8	5.5	23.7	7.9	43.4	27.1
CADC	45.6	22.1	68.2	16.6	34.5	15.4	50.1	35.9
CDTL	41.6	13.1	65.5	7.6	29.7	10.2	44.9	30.4
CADC + CDTL	46.2	22.1	68.2	16.5	34.8	17.5	50.5	36.5

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表 9 SunnyDay→NightRainy数据集上基于YOLOv3的消融实验结果(%)

Table 9 The results of ablation experiment on SunnyDay→NightRainy dataset based on YOLOv3 (%)

方法	bus	bike	car	motor	person	rider	truck	mAP
SO	39.2	5.1	44.2	0.2	14.8	6.9	30.7	20.2
CADC	44.4	8.1	50.9	0.6	20.2	11.3	38.3	24.8
CDTL	40.4	8.2	45.8	0.6	16.2	7.2	33.4	21.7
CADC + CDTL	45.0	8.2	51.1	4.0	20.9	9.6	37.9	25.3

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表 10 SunnyDay→NightRainy数据集上基于YOLOv5s的消融实验结果(%)

Table 10 The results of ablation experiment on SunnyDay→NightRainy dataset based on YOLOv5s (%)

方法	bus	bike	car	motor	person	rider	truck	mAP
SO	25.4	3.2	36.3	0.2	9.1	4.4	20.8	14.2
CADC	38.7	8.3	42.7	0.3	12.3	6.4	32.0	20.1
CDTL	34.3	6.2	44.2	0.5	11.2	8.7	30.3	19.3
CADC + CDTL	40.7	9.3	45.0	0.6	12.8	9.2	32.5	21.5

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表 11 KITTI→CityScapes和Sim10k→CityScapes数据集上的对比实验结果(%)

Table 11 The results of different methods on KITTI→CityScapes and Sim10k→CityScapes datasets (%)

方法		KITTI	Sim10k
YOLOv3	SO	59.6	58.5
	CADC	60.5	59.6
	CDTL	60.5	60.8
	CADC + CDTL	61.1	59.8
	Oracle	64.7	64.7
YOLOv5s	SO	54.0	53.1
	CADC	59.5	58.6
	CDTL	59.0	60.3
	CADC + CDTL	60.0	59.0
	Oracle	65.9	65.9

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表 12 本文方法在VOC→Clipart1k上的实验(%)

Table 12 The experiment of our method on VOC→Clipart1k (%)

方法	aero	bcycle	bird	boat	bottle	bus	car	cat	chair	cow	table	dog	hrs	bike	prsn	plnt	sheep	sofa	train	tv	mAP
I3Net	23.7	66.2	25.3	19.3	23.7	55.2	35.7	13.6	37.8	35.5	25.4	13.9	24.1	60.3	56.3	39.8	13.6	34.5	56.0	41.8	35.1
I3Net + CDTL	23.3	61.6	27.8	17.1	24.7	54.3	39.8	12.3	41.4	34.1	32.2	15.5	27.6	77.9	57.0	37.4	5.50	31.3	51.8	47.8	36.0
I3Net + CDTL + ${\rm{CADC}}^*$	31.2	60.4	31.8	19.4	27.0	63.3	40.7	13.7	41.1	38.4	27.2	18.0	25.5	67.8	54.9	37.2	15.5	36.4	54.8	47.8	37.6

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表 13 本文方法在VOC→Comic2k上的实验(%)

Table 13 The experiment of our method on VOC→Comic2k (%)

方法	bike	bird	car	cat	dog	person	mAP
I3Net	44.9	17.8	31.9	10.7	23.5	46.3	29.2
I3Net + CDTL	43.7	15.1	31.5	11.7	18.6	46.9	27.9
I3Net + CDTL + CADC^*	47.8	16.0	33.8	15.1	24.4	43.5	30.1

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表 14 本文方法在VOC→Watercolor2k上的实验(%)

Table 14 The experiment of our method on VOC→Watercolor2k (%)

方法	bike	bird	car	cat	dog	person	mAP
I3Net	81.3	49.6	43.6	38.2	31.3	61.7	51.0
I3Net + CDTL	79.5	47.2	41.7	33.5	35.4	60.3	49.6
I3Net + CDTL + CADC^*	84.1	45.3	46.6	32.9	31.4	61.4	50.3

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表 15 像素级对齐对网络的影响(%)

Table 15 The impact of pixel alignment to network (%)

方法	检测器	C→F	K→C	S→C
CDTL + CADC	YOLOv3	35.9	59.8	58.4
CDTL + CADC + $D_{{\rm{pixel}}}$	YOLOv3	37.2	60.5	59.6
CDTL + CADC	YOLOv5s	32.7	58.9	56.8
CDTL + CADC + $D_{{\rm{pixel}}}$	YOLOv5s	34.1	59.5	58.6

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表 16 通道注意力域分类器中损失函数的选择

Table 16 The choice of loss function in channel attention domain classifier

检测器	$F_1$	$F_2$	$F_3$	mAP (%)
YOLOv3/v5s	CE	CE	CE	35.8/32.7
YOLOv3/v5s	CE	CE	FL	36.4/33.2
YOLOv3/v5s	CE	FL	FL	37.2/34.1
YOLOv3/v5s	FL	FL	FL	37.0/33.5

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