基于多尺度掩码分类域自适应网络的跨域目标检测算法

doi:10.19562/j.chinasae.qcgc.2022.09.004

Abstract

Abstract:

Aiming at the problems of multi-scale and domain negative transfer caused by the contradiction between domain discriminability and invariance in unsupervised domain adaptive object detection， a multi-scale mask classification domain adaptive network （MMCN）， that can alleviate the negative transfer of domain， is proposed in this paper. Firstly the adversarial training of image-level domain is performed on multiple intermediate layers on backbone network. Then a region proposal mask is added to the image-level feature map as a supplementary information to supplement instance features. Finally a sub-category instance-level domain classifier is put forward to enable the network to extract effective domain-invariant information as much as possible on the premise of ensuring domain discriminability. The results of verification on both Cityscapes and FoggyCityscapes datasets show that the mean average precision of domain classification with MMCN proposed is 13.2 percentage points higher than that with DA-FasterRCNN， significantly enhancing the domain adaptive capability of network.

Key words: object detection, domain adaptive network, deep learning, adversarial learning

Jie Hu,Boyuan Xu,Zongquan Xiong,Minjie Chang,Di Guo,Lihao Xie. Cross-Domain Object Detection Algorithm Based on Multi-scale Mask Classification Domain Adaptive Network[J].Automotive Engineering, 2022, 44(9): 1327-1338.

Figures/Tables 15

方法	$L 1$	$L 2$	$L 3$	人	骑手	汽车	货车	公交车	火车	摩托车	自行车	mAP/%
	$√$			35.4	45.0	51.2	31.8	45.6	36.8	32.9	38.0	39.6
MMCN			$√$	34.2	45.3	51.4	24.4	48.2	31.2	37.5	37.4	38.7
	$√$		$√$	33.7	46.6	51.6	29.1	43.4	39.8	30.5	38.5	39.2
	$√$	$√$	$√$	25.7	42.5	35.9	18.5	28.3	18.0	23.3	22.1	26.8
	$√$	$√$		33.4	45.3	51.2	29.6	44.6	33.5	28.9	34.7	37.7
		$√$	$√$	34.7	47.6	51.2	28.1	48.2	35.2	33.1	36.8	39.4
		$√$		33.3	45.8	51.2	32.1	47.3	38.0	33.8	37.3	39.9

多尺度	区域提议掩码+补充信息	实例分类域对齐	mAP/%
$×$	$×$	$×$	31.7
$×$	$×$	$√$	34.7
$√$	$×$	$×$	36.5
$√$	$√$	$×$	39.9
$√$	$×$	$√$	39.3
$√$	$√$	$√$	40.8

References 32

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方法	人	骑手	汽车	货车	公交车	火车	摩托车	自行车	mAP/%
Faster R-CNN	24.1	33.1	34.3	4.1	22.3	3.0	15.3	26.5	20.3
DA-Faster［19］	25.0	31.0	40.5	22.1	35.3	20.2	20.0	27.1	27.6
StrongWeak［26］	29.9	42.3	43.5	24.5	36.2	32.6	30.0	35.3	34.3
DD-MRL［29］	30.8	40.5	44.3	27.2	38.4	34.5	28.4	32.2	34.6
ATF［30］	34.6	47.0	50.0	23.7	43.3	38.7	33.4	38.8	38.7
VDD［31］	33.4	44.0	51.7	33.9	52.0	34.7	34.2	36.8	40.0
MMCN（本文）	33.4	46.8	51.9	29.1	48.4	43.2	36.0	37.4	40.8

方法	权重	人	骑手	汽车	货车	公交车	火车	摩托车	自行车	mAP/%
MMCN	0.1	33.5	44.6	50.9	28.0	44.5	38.3	34.3	35.1	38.6
	0.2	33.4	47.1	50.9	30.1	44.9	35.6	31.9	36.1	38.8
	0.3	26.3	41.5	32.4	21.9	32.4	9.4	28.3	23.4	28.4
	0.4	33.4	46.1	50.1	26.1	41.7	23.0	32.6	32.5	35.7
	0.5	33.5	45.2	50.5	33.4	43.6	40.8	30.9	36.4	39.3
	0.6	26.0	43.1	43.8	20.2	34.3	14.6	23.4	22.6	28.5
	0.7	32.9	46.5	50.3	29.7	45.2	36.7	29.2	35.4	38.2
	0.8	33.4	46.2	50.9	27.3	46.8	34.8	33.0	35.3	38.5
	0.9	33.6	46.3	50.7	29.8	46.2	31.6	33.3	37.4	38.6

方法	中间层1	中间层2	人	骑手	汽车	货车	公交车	火车	摩托车	自行车	mAP/%
MMCN	7	9	33.7	46.0	50.9	28.2	46.6	36.4	34.6	36.3	39.1
	5	9	33.5	46.7	50.7	26.8	43.9	24.0	32.9	36.2	36.8
	7	8	33.1	45.4	50.9	25.3	45.7	19.7	35.0	36.8	36.5
	6	8	33.3	47.0	51.3	30.2	46.3	23.9	29.2	36.5	37.2
	5	8	33.4	45.0	51.0	31.1	45.5	22.9	35.5	36.4	37.6
	7	10	33.3	46.6	51.1	29.4	44.8	35.0	34.0	36.0	38.8
	6	10	33.3	44.7	51.0	30.0	44.4	37.3	33.4	35.8	38.7
	5	10	32.9	44.6	48.6	23.4	39.2	25.8	30.1	32.4	34.6
	6	9	33.4	46.8	51.9	29.1	48.4	43.2	36.0	37.4	40.8

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Cross-Domain Object Detection Algorithm Based on Multi-scale Mask Classification Domain Adaptive Network

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