一种改进变换网络的域自适应语义分割网络

doi:10.16183/j.cnki.jsjtu.2019.307

摘要/Abstract

摘要：

语义标签的人工标注成本高,耗时长,基于域自适应的非监督语义分割是非常必要的.针对间隙大的场景或像素易限制模型训练、降低语义分割精度的问题,通过分阶段训练和可解释蒙版消除大间隙图片和像素的干扰,提出了一种改进变换网络的域自适应语义分割网络(DA-SSN).首先,针对部分源图到目标图的域间隙大、网络模型训练困难的问题,利用训练损失阈值划分大间隙的源图数据集,提出一种分阶段的变换网络训练策略,在保证小间隙源图的语义对齐基础上,提高了大间隙源图的变换质量.然后,为了进一步缩小源图中部分像素与目标图域间间隙,提出一种可解释蒙版.通过预测每个像素在源图域和目标图域之间的间隙缩小置信度,忽略对应像素的训练损失,以消除大间隙像素对其他像素语义对齐的影响,使得模型训练只关注高置信度像素的域间隙.结果表明,所提算法相比于原始的域自适应语义分割网络的分割精度更高.与其他流行算法的结果相比,所提方法获得了更高质量的语义对齐,表明了所提方法精度高的优势.

关键词: 计算机视觉, 域自适应语义分割, 域间隙, 语义信息整合, 可解释蒙版, 分阶段训练

Abstract:

Due to the high cost and time-consumption of artificial semantic tags, domain-based adaptive semantics segmentation is very necessary. For scenes with large gaps or pixels, it is easy to limit model training and reduce the accuracy of semantic segmentation. In this paper, a domain adaptive semantic segmentation network (DA-SSN) using the improved transformation network is proposed by eliminating the interference of large gap pictures and pixels through staged training and interpretable masks. First, in view of the problem of large domain gaps from some source graphs to target graphs and the difficulty in network model training, the training loss threshold is used to divide the source graph dataset with large gaps, and a phased transformation network training strategy is proposed. Based on the ensurance of the semantic alignment of small gap source images, the transformation quality of large gap source images is improved. In addition, in order to further reduce the gap between some pixels in the source image and the target image area, an interpretable mask is proposed. By predicting the gap between each pixel in the source image domain and the target image domain, the confidence is reduced, and the training loss of the corresponding pixel is ignored to eliminate the influence of large gap pixels on the semantic alignment of other pixels, so that model training only focuses on the domain gap of high-confidence pixels. The results show that the proposed algorithm has a higher segmentation accuracy than the original domain adaptive semantic segmentation network. Compared with the results of other popular algorithms, the proposed method obtains a higher quality semantic alignment, which shows the advantages of the proposed method with high accuracy.

Key words: computer vision, domain adaptive semantics segmentation, domain gap, semantic information integration, interpretable mask, staged training

中图分类号:

TP391.4

张峻宁, 苏群星, 王成, 徐超, 李一宁. 一种改进变换网络的域自适应语义分割网络[J]. 上海交通大学学报, 2021, 55(9): 1158-1168.

ZHANG Junning, SU Qunxing, WANG Cheng, XU Chao, LI Yining. A Domain Adaptive Semantic Segmentation Network Based on Improved Transformation Network[J]. Journal of Shanghai Jiao Tong University, 2021, 55(9): 1158-1168.

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方法	训练损失	平均交并比
原始方法	0.383	41.3
D₂	0.339	42.1

方法	损失阈值ϑ	平均交并比
D₂	0.38	41.4
	0.36	41.6
	0.34	42.0
	0.32	42.1
	0.30	41.9

方法	域间间隙	平均交并比
原始方法	0.383	41.3
D₃	0.015	42.2
本文方法	0.006	42.4

网络	算法	road	sidewalk	Building	wall	fence	pole	t-light	t-sign	wegetation	terrain
DeepLab v2^[14]	Cycada	85.2	37.2	76.5	21.8	15.0	23.8	22.9	21.5	80.5	31.3
	DCAN	82.3	26.7	77.4	23.7	20.5	20.4	30.3	15.9	80.9	25.4
	CLAN	88.0	30.6	79.2	23.4	20.5	26.1	23.0	14.8	81.6	34.5
	BLD	89.2	40.9	81.2	29.1	19.2	14.2	29.0	19.6	83.7	35.9
	本文	90.9	42.3	82.1	30.8	18.5	16.7	31.5	20.8	85.9	33.7
Resnet^[24]	Cycada	86.7	35.6	80.1	19.8	17.5	38.0	39.9	41.5	82.7	27.9
	DCAN	85.0	30.8	81.3	25.8	21.2	22.2	25.4	26.6	83.4	36.7
	CLAN	87.0	27.1	79.6	27.3	23.3	28.3	35.5	24.2	83.6	27.4
	BLD	91.0	44.7	84.2	34.6	27.6	30.2	36.0	36.0	85.0	43.6
	本文	92.2	42.3	83.5	36.2	28.0	31.8	36.7	36.2	85.7	44.6
网络	算法	sky	person	rider	car	truck	bus	train	motorbike	bicycle	mIoU
DeepLab v2^[14]	Cycada	60.7	50.5	9.0	76.9	17.1	28.2	4.5	9.8	0	35.4
	DCAN	69.5	52.6	11.1	79.6	24.9	21.2	1.30	17.0	6.70	36.2
	CLAN	72.0	45.8	7.9	80.5	26.6	29.9	0.0	10.7	0.0	36.6
	BLD	80.7	54.7	23.3	82.7	25.8	28.0	2.3	25.7	19.9	41.3
	本文	83.3	55.9	23.6	82.1	27.7	29.4	2.2	26.8	21.2	42.4
Resnet^[24]	Cycada	73.6	64.9	19	65.0	12.0	28.6	4.5	31.1	42.0	42.7
	DCAN	76.2	58.9	24.9	80.7	29.5	42.9	2.50	26.9	11.6	41.7
	CLAN	74.2	58.6	28.0	76.2	33.1	36.7	6.7	31.9	31.4	43.2
	BLD	83.0	58.6	31.6	83.3	35.3	49.7	3.3	28.8	35.6	48.5
	本文	81.2	59.8	32.7	84.1	36.3	49.9	3.0	30.7	37.4	49.1