一种用于互惠目标检测与实例分割的深层架构

doi:10.11996/JG.j.2095-302X.2024040745

图学学报 ›› 2024, Vol. 45 ›› Issue (4): 745-759.DOI: 10.11996/JG.j.2095-302X.2024040745

• 图像处理与计算机视觉 • 上一篇下一篇

一种用于互惠目标检测与实例分割的深层架构

宫永超¹^,²(), 沈旭昆¹^,²^,³()

1.北京航空航天大学计算机学院，北京 100191
2.北京航空航天大学虚拟现实技术与系统国家重点实验室，北京 100191
3.北京航空航天大学新媒体艺术与设计学院，北京 100191

收稿日期:2023-12-18 接受日期:2024-05-03 出版日期:2024-08-31 发布日期:2024-09-03
通讯作者:沈旭昆(1965-)，男，教授，博士。主要研究方向为计算机图形学和虚拟现实等。E-mail：xkshen@buaa.edu.cn
第一作者:宫永超(1988-)，男，博士后，博士。主要研究方向为计算机视觉与深度学习。E-mail：gyc_ustc@163.com

A deep architecture for reciprocal object detection and instance segmentation

GONG Yongchao¹^,²(), SHEN Xukun¹^,²^,³()

1. School of Computer Science and Engineering, Beihang University, Beijing 100191, China
2. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China
3. School of New Media Art and Design, Beihang University, Beijing 100191, China

Received:2023-12-18 Accepted:2024-05-03 Published:2024-08-31 Online:2024-09-03
Contact: SHEN Xukun (1965-), professor, Ph.D. His main research interests cover computer graphics and virtual reality, etc. E-mail：xkshen@buaa.edu.cn
First author：GONG Yongchao (1988-), Ph.D. His main research interests cover computer vision and deep learning. E-mail：gyc_ustc@163.com

摘要/Abstract

摘要：

目标检测与实例分割是计算机视觉中2种重要且关系紧密的任务，但其间的关联在大多数工作中还未得到充分的探索。为此，提出了RDSNet，一种用于互惠目标检测与实例分割的深层架构。为了实现这2种任务之间协同优化，设计了一个双流式结构来联合学习目标级别和像素级别的特征表达，分别用于编码目标级别和像素级别的信息，并在双流之间引入了3个模块来实现二者的相互作用，让目标信息辅助实例分割，像素信息辅助目标检测。通过相关模块提供一种计算目标级和像素级特征相似度的手段，以便于驱动属于同一目标的特征尽可能一致，提高实例掩码的精度。裁剪模块利用目标信息为像素级感知引入实例的概念和平移变化性，以便于更准确地区分不同实例和减少背景噪声。为了进一步提高检测框与目标的贴合程度，提出了基于掩码的边界精细化模块来对掩码和检测框做融合，利用掩码的准确性优势修正检测框的误差。在COCO数据集上的大量实验分析和对比证实了RDSNet的有效性和高效性。此外，通过在边界精细化模块引入掩码打分策略，以新的方式实现了实例分割对目标检测的辅助，使RDSNet的性能得到了进一步提升。

关键词: 目标检测, 实例分割, 互惠关系, 特征表达, 边界精细化

Abstract:

Object detection and instance segmentation are two fundamental and closely correlated tasks in computer vision, yet their relations have not been fully explored in most previous works.For this reason, we presented the reciprocal object detection and instance segmentation network (RDSNet), a novel deep architecture. To reciprocate between these two tasks, we designed a two-stream structure to learn feature representations jointly at both the object level (i.e., bounding boxes) and the pixel level (i.e., instance masks), thus encoding object- and pixel-level information respectively. Moreover, three new modules were introduced for the interactions between the two streams, allowing object-level information to assist instance segmentation and pixel-level information to assist object detection. Specifically, a correlation module was used to measure the similarity between object- and pixel-level features, promoting the consistency in features belonging to the same object and enhancing the accuracy of instance masks consequently. We proposed a cropping module to better distinguish different instances and reduce background noise, by introducing the awareness of instance and translation variance to pixel-level perception. To further refine the alignment between bounding boxes and their corresponding objects, a mask-based boundary refinement module (MBRM) was proposed for the fusion of bounding boxes and instance masks, which had the potential to correct the errors in bounding boxes with the help of instance masks. Extensive experimental analyses and comparisons on the COCO dataset demonstrated the effectiveness and efficiency of RDSNet. In addition, we further improved the performance of RDSNet by integrating the mask scoring strategy into MBRM, which allowed object detection to benefit from instance segmentation in a new way.

Key words: object detection, instance segmentation, reciprocal relation, feature representation, boundary refinement

中图分类号:

TP391

宫永超, 沈旭昆. 一种用于互惠目标检测与实例分割的深层架构[J]. 图学学报, 2024, 45(4): 745-759.

GONG Yongchao, SHEN Xukun. A deep architecture for reciprocal object detection and instance segmentation[J]. Journal of Graphics, 2024, 45(4): 745-759.

图/表 18

图1 检测框误差导致的实例分割误差示例((a)，(b)检测框没有完全包围目标；(c)，(d)检测框不贴合目标)

Fig. 1 Illustration of errors in instance segmentation due to localization errors in object detection ((a), (b) Boxes do not fully enclose objects; (c), (d) Boxes do not enclose objects tightly)

图2 RDSNet整体结构图 c表示类别数，k表示每个位置的锚点数，d表示特征表达的维度，*表示卷积算子

Fig. 2 Illustration of the architecture of RDSNet

图3 目标与像素的特征表达及二者之间关系示意图

Fig. 3 Illustration of the representations of objects and pixels

图4 裁剪模块中不同检测框下负样本难例情况展示

Fig. 4 Illustration of hard negative samples by different bounding box strategies in the cropping module

图5 MBRM原理示意图

Fig. 5 Illustration of MBRM

表1 COCO test-dev数据集上的实例分割评测结果

Table 1 Instance segmentation results on COCO test-dev. P means Titan XP or 1080Ti, and V means Tesla V100

类型	方法	尺寸	帧率	AP^m	AP₅₀^m	AP₇₅^m	AP_S^m	AP₅₀^M	AP_L^m
两阶段	Mask R-CNN^[4]	800	9.5 (V)	36.2	58.3	38.6	16.7	38.8	51.5
	MS R-CNN^[27]	800	9.1 (V)	37.4	57.9	40.4	17.3	39.5	53.0
	RetinaMask^[7]	800	6.0 (V)	34.7	55.4	36.9	14.3	36.7	50.5
单阶段	FCIS^[12]	600	6.6 (P)	29.2	49.5	-	7.1	31.3	50.0
	YOLACT^[10]	550	33.0 (P)	29.8	48.5	31.2	9.9	31.3	47.7
	YOLACT++^[28]	550	27.0 (P)	34.6	53.8	36.9	11.9	36.8	55.1
	PolarMask^[29]	550	23.9 (P)	30.4	51.9	31.0	13.4	32.4	42.8
	RDSNet	550	32.0 (P)	32.1	53.0	33.4	11.0	33.8	51.0
	MEInst^[30]	800	12.8 (P)	33.9	56.2	35.4	19.8	36.1	42.3
	SOLO^[13]	800	10.4 (V)	37.8	59.5	40.4	16.4	40.6	54.2
	TensorMask^[14]	800	2.6 (V)	37.3	59.5	39.5	17.5	39.3	51.6
	RDSNet (以文献[2]为基线)	800	8.8 (V)	36.4	57.9	39.0	16.4	39.5	51.6
	RDSNet (以文件[31]为基线)	800	7.5 (P)	37.5	59.3	40.4	16.9	40.5	53.0
	RDSNet+ (以文献[2]为基线)	800	8.8 (V)	37.2	59.1	40.2	16.8	41.2	52.8
	RDSNet+ (以文献[31]为基线)	800	7.5 (P)	38.5	60.4	41.8	17.3	41.8	54.3
性能上限	RDSNet (基于真值框)	800	-	58.7	68.5	63.1	49.2	59.0	75.4

表3 COCO test-dev数据集上的目标检测评测结果

Table 3 Object detection results on COCO test-dev

类型	方法		尺寸	主干网络	帧率	AP^bb	AP₅₀^bb	AP₇₅^bb	AP_S^bb	AP_M^bb	AP_L^bb
两阶段	Mask R-CNN^[4]		800	R-101	9.5 (V)	39.7	61.6	43.2	23.0	43.2	49.7
	Cascade R-CNN^[32]		800	R-101	6.8 (V)	43.1	61.5	46.9	24.0	45.9	55.4
	HTC^[33]		800	R-101	4.1 (V)	45.1	64.3	49.0	25.2	48.0	58.2
单阶段	YOLOv3^[34]		608	D-53	19.8 (P)	33.0	57.9	34.3	18.3	35.4	41.9
	RefineDet^[35]		512	R-101	9.1 (P)	36.4	57.5	39.5	16.6	39.9	51.4
	CornerNet^[36]		512	H-104	4.4 (P)	40.5	57.8	45.3	20.8	44.8	56.7
	RDSNet	基线^[2]	800	R-101	10.9 (V)	38.1	58.5	40.8	21.2	41.5	48.2
		w/o MBRM			8.8 (V)	39.4	60.1	42.5	22.1	42.6	49.9
		with MBRM			8.5 (V)	40.3	60.1	43.0	22.1	43.5	51.5
		基线^[31]	800	R-101	9.1 (P)	42.0	62.4	46.5	24.6	44.8	53.3
		w/o MBRM			7.5 (P)	42.3	62.5	46.8	24.7	44.8	53.5
		with MBRM			7.3 (P)	43.2	63.7	48.0	25.0	45.2	56.1
	RDSNet+	以文献[2]为检测器	800	R-101	8.4 (V)	41.4	60.9	44.3	22.5	44.0	52.4
	RDSNet+	以文献[31]为检测器	800	R-101	7.2 (P)	44.3	64.1	49.2	25.3	45.9	56.8

图6 COCO val2017数据集上的定性结果展示((a) Mask R-CNN；(b)无MBRM的RDSNet；(c)完整RDSNet)

Fig. 6 Visual comparisons of some results on COCO val2017 ((a) Mask R-CNN; (b) A variant of RDSNet w/o MBRM; (c) RDSNet)

图7 不同方法在COCO test-dev数据集上速度和精度的对比((a)实例分割方法对比；(b)目标检测方法对比)

Fig. 7 Comparisons of speed and APm on COCO test-dev by different methods ((a) Instance segmentation methods; (b) Object detection methods)

表2 COCO val2017数据集上裁剪模块有效性验证结果

Table 2 Demonstration of the effectiveness of the cropping module on COCO val2017

No.	方法	模块	TE	OHEM	IE	帧率	AP^m
1	YOLACT^[10]	LC				33	29.9
2	RDSNet_s	Corr				32	31.0_+1.1
3					√		30.0
4			√				30.7
5				√			31.2
6			√		√		30.8
7			√	√			31.6
8			√	√	√		31.8_+1.9
9	RDSNet_f	Corr	√		√	29	28.8
10	RDSNet_f	Corr	√	√	√	29	28.5

图8 COCO val2017数据集上裁剪模块采用不同正负样本比例和难例挖掘策略下的实例分割结果

Fig. 8 Instance segmentation results on COCO val2017 obtained by different ratios of positive/negative samples and OHEM strategies in the cropping module

图9 COCO val2017数据集上的像素特征表达可视化展示((a)原图；(b)像素特征表达可视化图)

Fig. 9 Visualization of pixel representation of some results on COCO val2017 ((a) Original image; (b) Visualization of feature representation)

图10 COCO val2017数据集上的定性结果展示((a)，(c)无MBRM的RDSNet；(b)，(d)完整的RDSNet)

Fig. 10 Visual comparisons of some results on COCO val2017 ((a), (c) RDSNet w/o MBRM; (b), (d) RDSNet)

图11 MBRM对超参数的敏感性分析

Fig. 11 Hyper-parameter sensitivity analyses of MBRM

表4 COCO val2017数据集上MBRM有效性验证结果

Table 4 Demonstration of the effectiveness of MBRM on COCO val2017

方法	AP^bb	AP_S^bb	AP_M^bb	AP_L^bb
RetinaNet^[2]	35.9	17.1	39.7	53.3
BB-of-Mask	34.2_−1.7	11.8_−5.3	37.7_−2.0	55.1_+1.8
MBRM	37.2_+1.3	16.9_−0.2	40.8_+1.1	56.5_+3.2

图12 COCO test-dev数据集上所有预测和真值匹配上的框的IoU分布统计

Fig. 12 Distribution of IoUs for all matched bounding boxes (prediction and ground truth) on COCO test-dev

图13 COCO test-dev数据集上关于检测器性能对RDSNet性能影响的结果展示((a)不同基线检测器指标；(b)不同检测器下的RDSNet目标检测指标；(c)不同检测器下的RDSNet实例分割指标)

Fig. 13 Quantitative results on COCO test-dev for verifying the impact of the performance of detectors on RDSNet ((a) Results of different base detectors; (b) Results of RDSNet object detection with different base detectors; (c) Results of RDSNet instance segmentation with different base detectors)

图14 基于RDSNet改进的RDSNet+示意图

Fig. 14 Illustration of RDSNet+, an updated version of RDSNet

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一种用于互惠目标检测与实例分割的深层架构

A deep architecture for reciprocal object detection and instance segmentation

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