基于注意力機制和循環(huán)域三元損失的域自適應目標檢測

周洋; 韓冰; 高新波; 楊錚; 陳瑋銘

doi:10.16383/j.aas.c220938

基于注意力機制和循環(huán)域三元損失的域自適應目標檢測

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

周洋^1,,
韓冰^1,,
高新波^{1, 2,},
楊錚^1,,
陳瑋銘^1,

1.
西安電子科技大學(xué)電子工程學(xué)院西安 710071
2.
重慶郵電大學(xué)圖像認知重慶市重點(diǎn)實(shí)驗室重慶 400065

基金項目: 國家自然科學(xué)基金(62076190, 41831072, 62036007), 陜西省重點(diǎn)創(chuàng )新產(chǎn)業(yè)鏈基金(2022ZDLGY01-11), 西安市重點(diǎn)產(chǎn)業(yè)鏈技術(shù)攻關(guān)項目(23ZDCYJSGG0022-2023), 國家空間科學(xué)數據中心青年開(kāi)放課題基金(NSSDC2302005)資助

詳細信息

作者簡(jiǎn)介:
周洋：西安電子科技大學(xué)電子工程學(xué)院碩士研究生. 2020年獲得西南石油大學(xué)電子信息工程專(zhuān)業(yè)學(xué)士學(xué)位. 主要研究方向為計算機視覺(jué)和域自適應目標檢測. E-mail: yzhou_6@stu.xidian.edu.cn

韓冰：西安電子科技大學(xué)電子工程學(xué)院教授. 主要研究方向為智能輔助駕駛系統, 視覺(jué)感知與認知, 空間物理與人工智能交叉. 本文通信作者. E-mail: bhan@xidian.edu.cn

高新波：西安電子科技大學(xué)教授. 主要研究方向為機器學(xué)習, 圖像處理, 計算機視覺(jué), 模式識別和多媒體內容分析. E-mail: xbgao@ieee.org

楊錚：西安電子科技大學(xué)電子工程學(xué)院博士研究生. 2017年獲得西安電子科技大學(xué)智能科學(xué)與技術(shù)專(zhuān)業(yè)學(xué)士學(xué)位. 主要研究方向為深度學(xué)習, 目標跟蹤和強化學(xué)習. E-mail: zhengy@stu.xidian.edu.cn

陳瑋銘：西安電子科技大學(xué)電子工程學(xué)院碩士研究生. 2019年獲得西安電子科技大學(xué)機械設計制造及其自動(dòng)化專(zhuān)業(yè)學(xué)士學(xué)位. 主要研究方向為計算機視覺(jué), 目標檢測和遙感技術(shù). E-mail: wmchen@stu.xidian.edu.cn

計量
- 文章訪(fǎng)問(wèn)數: 658
- HTML全文瀏覽量: 266
- PDF下載量: 87
- 被引次數: 0
出版歷程
- 收稿日期: 2022-12-05
- 錄用日期: 2023-05-18
- 網(wǎng)絡(luò )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: 目前大多數深度學(xué)習算法都依賴(lài)于大量的標注數據并欠缺一定的泛化能力. 無(wú)監督域自適應算法能提取到已標注數據和未標注數據間隱式共同特征, 從而提高算法在未標注數據上的泛化性能. 目前域自適應目標檢測算法主要為兩階段目標檢測器設計. 針對單階段檢測器中無(wú)法直接進(jìn)行實(shí)例級特征對齊導致一定數量域不變特征的缺失, 提出結合通道注意力機制的圖像級域分類(lèi)器加強域不變特征提取. 此外, 對于域自適應目標檢測中存在類(lèi)別特征的錯誤對齊引起的精度下降問(wèn)題, 通過(guò)原型學(xué)習構建類(lèi)別中心, 設計了一種基于原型的循環(huán)域三元損失(Cycle domain triplet loss, CDTL)函數, 從而實(shí)現原型引導的精細類(lèi)別特征對齊. 以單階段目標檢測算法作為檢測器, 并在多種域自適應目標檢測公共數據集上進(jìn)行實(shí)驗. 實(shí)驗結果證明該方法能有效提升原檢測器在目標域的泛化能力, 達到比其他方法更高的檢測精度, 并且對于單階段目標檢測網(wǎng)絡(luò )具有一定的通用性.
- 無(wú)監督域自適應 /
- 注意力機制 /
- 循環(huán)域三元損失函數 /
- 目標檢測
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

圖 1 基于注意力機制和循環(huán)域三元損失的域自適應目標檢測算法流程

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

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圖 2 循環(huán)域自適應三元損失函數原理

Fig. 2 Principle of cycle domain adaptive TripleLoss

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圖 3 本文方法在CityScapes→FoggyCityScapes上的主觀(guān)檢測結果

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

下載: 全尺寸圖片幻燈片

圖 4 本文方法在SunnyDay→DuskRainy和SunnyDay→NightRainy上的主觀(guān)檢測結果

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

下載: 全尺寸圖片幻燈片

圖 5 本文方法在KITTI→CityScapes和Sim10k→CityScapes上的消融實(shí)驗結果

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

下載: 全尺寸圖片幻燈片

圖 6 本文方法在VOC→Clipart1k上的主觀(guān)結果

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

下載: 全尺寸圖片幻燈片

圖 7 不同循環(huán)迭代訓練次數在YOLOv3和YOLOv5s檢測器上的結果

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

下載: 全尺寸圖片幻燈片

表 1 不同方法在CityScapes→FoggyCityScapes數據集上的對比實(shí)驗結果(%)

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

方法	檢測器	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: “—”表示該方法沒(méi)有進(jìn)行此實(shí)驗; (v3)表示檢測器為YOLOv3; (v5)表示檢測器為YOLOv5s; 加粗數值表示對比實(shí)驗中的最佳結果.

下載: 導出CSV

表 2 不同方法在SunnyDay→DuskRainy數據集上的對比實(shí)驗結果(%)

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

方法	檢測器	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的漲幅程度.

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表 3 不同方法在SunnyDay→NightRainy數據集上的對比實(shí)驗結果(%)

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

方法	檢測器	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數據集上的對比實(shí)驗結果(%)

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

下載: 導出CSV

表 5 CityScapes→FoggyCityScapes數據集上基于YOLOv3的消融實(shí)驗結果(%)

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數據集上基于YOLOv5s的消融實(shí)驗結果(%)

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數據集上基于YOLOv3的消融實(shí)驗結果(%)

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數據集上基于YOLOv5s的消融實(shí)驗結果(%)

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

下載: 導出CSV

表 9 SunnyDay→NightRainy數據集上基于YOLOv3的消融實(shí)驗結果(%)

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數據集上基于YOLOv5s的消融實(shí)驗結果(%)

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

下載: 導出CSV

表 11 KITTI→CityScapes和Sim10k→CityScapes數據集上的對比實(shí)驗結果(%)

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上的實(shí)驗(%)

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上的實(shí)驗(%)

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上的實(shí)驗(%)

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 像素級對齊對網(wǎng)絡(luò )的影響(%)

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

方法	檢測器	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 通道注意力域分類(lèi)器中損失函數的選擇

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

檢測器	$F_1$	$F_2$	$F_3$	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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