集成多种上下文与混合交互的显著性目标检测

夏晨星; 陈欣雨; 孙延光; 葛斌; 方贤进; 高修菊; 张艳

doi:10.11999/JEIT230719

集成多种上下文与混合交互的显著性目标检测

doi: 10.11999/JEIT230719

夏晨星^{1, 2, 3},
陈欣雨^1, ,,
孙延光⁴,
葛斌¹,
方贤进^{1, 5},
高修菊⁶,
张艳⁷

1.
安徽理工大学计算机科学与工程学院淮南 232001
2.
安徽璞华大数据技术有限公司淮南 230031
3.
合肥综合性国家科学中心能源研究院合肥 230601
4.
南京理工大学计算机科学与工程学院南京 210094
5.
合肥综合性国家科学中心人工智能研究院合肥 230601
6.
安徽理工大学电气与信息工程学院淮南 232001
7.
安徽大学电子信息工程学院合肥 230601

基金项目: 国家自然科学基金(62102003)，安徽省自然科学基金(2108085QF258)，安徽省博士后基金(2022B623)，淮南市科技计划项目(2023A316)，安徽高校协同创新项目(GXXT-2021-006, GXXT-2022-038)，安徽理工大学青年科学研究基金一般项目(xjyb2020-04)，中央引导地方科技发展专项资金(202107d06020001)

详细信息

作者简介:
夏晨星：男，副教授，研究方向为计算机视觉

陈欣雨：女，硕士生，研究方向为伪装目标检测

孙延光：男，博士生，研究方向为显著性目标检测

葛斌：男，教授，研究方向为图像加密、计算机视觉

方贤进：男，教授，研究方向为人工智能、信息安全

高修菊：女，讲师，研究方向为计算机视觉

张艳：女，副教授，研究方向为模式识别与图像处理

通讯作者:
陈欣雨　18900573647@163.com

中图分类号: TN911.7; TP391.41
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- 文章访问数: 462
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- 被引次数: 0
出版历程
- 收稿日期: 2023-07-18
- 修回日期: 2024-01-06
- 网络出版日期: 2024-01-28
- 刊出日期: 2024-07-29

Integrating Multiple Context and Hybrid Interaction for Salient Object Detection

XIA Chenxing^{1, 2, 3},
CHEN Xinyu^{1
, ,},
SUN Yanguang⁴,
GE Bin¹,
FANG Xianjin^{1, 5},
GAO Xiuju⁶,
ZHANG Yan⁷

1.
College of Computer Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China
2.
Anhui Puhua Big Data Technology Co., Ltd., Huainan 230031, China
3.
Institute of Energy, Hefei Comprehensive National Science Center, Hefei 230601, China
4.
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
5.
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230601, China
6.
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
7.
School of Electronics and Information Engineering, Anhui University, Hefei 230601, China

Funds: The National Natural Science Foundation of China (62102003), The Natural Science Foundation of Anhui Province (2108085QF258), Anhui Postdoctoral Science Foundation (2022B623), Huainan City Science and Technology Plan Project (2023A316), The University Synergy Innovation Program of Anhui Province (GXXT-2021-006, GXXT-2022-038), The University-level General Projects of Anhui University of Science and Technology (xjyb2020-04), The Central Guiding Local Technology Development Special Funds (202107d06020001)

摘要

摘要: 显著性目标检测目的是识别和分割图像中的视觉显著性目标，它是计算机视觉任务及其相关领域的重要研究内容之一。当下基于全卷积网络(FCNs)的显著性目标检测方法已经取得了不错的性能，然而现实场景中的显著性目标类型多变且尺寸不固定，这使得准确检测并完整分割出显著性目标仍然是一个巨大的挑战。为此，该文提出集成多种上下文和混合交互的显著性目标检测方法，通过利用密集上下文信息探索模块和多源特征混合交互模块来高效预测显著性目标。密集上下文信息探索模块采用空洞卷积、不对称卷积和密集引导连接渐进地捕获具有强关联性的多尺度和多感受野上下文信息，通过集成这些信息来增强每个初始多层级特征的表达能力。多源特征混合交互模块包含多种特征聚合操作，可以自适应交互来自多层级特征中的互补性信息，以生成用于准确预测显著性图的高质量特征表示。此方法在5个公共数据集上进行了性能测试，实验结果表明，该文方法在不同的评估指标下与19种基于深度学习的显著性目标检测方法相比取得优越的预测性能。
- 计算机视觉 /
- 显著性目标检测 /
- 全卷积网络 /
- 上下文信息
Abstract: Salient Object Detection (SOD) aims to recognize and segment visual salient objects in images, which is one of the important research contents in computer vision tasks and related fields. Existing Fully Convolutional Networks (FCNs)-based SOD methods have achieved good performance. However, the types and sizes of salient objects are variable and unfixed in real-world scenes, which makes it still a huge challenge to detect and segment salient objects accurately and completely. For that, in this paper, a novel integrating multiple context and hybrid interaction for SOD task is proposed to efficiently predict salient objects by collaborating Dense Context Information Exploration (DCIE) module and Multi-source Feature Hybrid Interaction (MFHI) module. The DCIE module uses dilated convolution, asymmetric convolution and dense guided connection to progressively capture the strongly correlated multi-scale and multi-receptive field context information, and enhances the expression ability of each initial input feature by aggregating context information. The MFHI module contains diverse feature aggregation operations, which can adaptively interact with complementary information from multi-level features to generate high-quality feature representations for accurately predicting saliency maps. The performance of the proposed method is tested on five public datasets. The performance of the proposed method is tested on five public datasets. Experimental results demonstrate that our method achieves superior prediction performance compared with 19 state-of-the-art SOD methods under different evaluation metrics.
- Computer vision /
- Salient Object Detection (SOD) /
- Fully Convolutional Networks (FCNs) /
- Context information

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图 1 一些方法与本文方法的预测显著性图

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图 2 本文显著性目标检测方法的完整流程图

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图 3 密集上下文信息探索模块

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图 4 多源特征混合交互模块

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图 5 不同显著性目标检测方法的PR曲线比较

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图 6 不同显著性目标检测方法的F_m曲线比较

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图 7 本文方法与10种最近的SOD方法进行视觉比较结果

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图 8 本文方法使用不同模块定性比较结果

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图 9 MFHI模块不同聚合策略定性比较结果

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图 10 不同模块预测显著性图比较

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图 11 DCIE模块不同结构预测显著性图比较

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表 1 MAE, AF_m和WF_m的定量比较结果

方法	ECSSD (1000)			PASCAL-S (850)			HKU-IS (4447)			DUT-OMRON (5168)			DUTS-TE (5019)
方法	MAE	AF_m	WF_m	MAE	AF_m	WF_m	MAE	AF_m	WF_m	MAE	AF_m	WF_m	MAE	AF_m	WF_m
Amulet₁₇	0.059	0.868	0.840	0.100	0.757	0.728	0.051	0.841	0.817	0.098	0.647	0.626	0.085	0.678	0.658
UCF₁₇	0.069	0.844	0.806	0.116	0.726	0.726	0.062	0.823	0.779	0.120	0.621	0.574	0.112	0.631	0.596
DGRL₁₈	0.046	0.893	0.871	0.077	0.794	0.772	0.041	0.875	0.851	0.066	0.711	0.688	0.054	0.755	0.748
BDMPM₁₈	0.045	0.869	0.871	0.074	0.758	0.774	0.039	0.871	0.859	0.064	0.692	0.681	0.049	0.746	0.761
PoolNet₁₉	0.039	0.915	0.896	0.075	0.815	0.793	0.032	0.900	0.883	0.056	0.739	0.721	0.040	0.809	0.807
CPD₁₉	0.037	0.917	0.898	0.071	0.820	0.794	0.033	0.895	0.879	0.056	0.747	0.719	0.043	0.805	0.795
AFNet₁₉	0.042	0.908	0.886	0.070	0.815	0.792	0.036	0.888	0.869	0.057	0.739	0.717	0.046	0.793	0.785
R2Net₂₀	0.038	0.914	0.899	0.069	0.817	0.793	0.033	0.896	0.880	0.054	0.744	0.728	0.041	0.801	0.804
GateNet₂₀	0.040	0.916	0.894	0.067	0.819	0.797	0.033	0.899	0.880	0.055	0.746	0.729	0.040	0.807	0.809
ITSD₂₀	0.035	0.895	0.911	0.066	0.785	0.812	0.031	0.899	0.894	0.061	0.756	0.750	0.041	0.804	0.824
MINet₂₀	0.034	0.924	0.911	0.064	0.829	0.809	0.029	0.909	0.897	0.056	0.756	0.738	0.037	0.828	0.825
SUCA₂₁	0.036	0.915	0.906	0.067	0.818	0.803	0.031	0.897	0.890	–	–	–	0.044	0.803	0.802
CANet₂₁	0.044	0.900	0.878	0.073	0.813	0.792	0.037	0.882	0.866	0.058	0.731	0.720	0.044	0.785	0.788
DSRNet₂₁	0.039	0.910	0.891	0.067	0.819	0.801	0.035	0.893	0.873	0.061	0.727	0.711	0.043	0.791	0.794
VST₂₁	0.033	0.920	0.910	0.061	0.829	0.816	0.029	0.900	0.897	0.058	0.756	0.755	0.037	0.818	0.828
DNA₂₂	0.042	0.891	0.883	0.079	0.790	0.772	0.035	0.863	0.864	0.063	0.694	0.696	0.046	0.747	0.765
DCENet₂₂	0.035	0.926	0.913	0.061	0.845	0.825	0.029	0.908	0.898	0.055	0.771	0.754	0.038	0.842	0.834
DNTDF₂₂	0.034	0.900	0.909	0.064	0.810	0.814	0.028	0.905	0.901	0.051	0.748	0.732	0.033	0.822	0.839
ICON₂₃	0.032	0.928	0.918	0.064	0.833	0.818	0.029	0.910	0.902	0.057	0.772	0.761	0.037	0.838	0.837
本文	0.032	0.935	0.922	0.060	0.841	0.822	0.026	0.923	0.911	0.049	0.778	0.759	0.034	0.862	0.846

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表 2 参数量、推理速度和模型内存的比较结果

方法	输入尺寸	参数量 (M)	推理速度 (帧/s)	模型内存 (MB)
Amulet	320×320	33.15	8	132
DGRL	384×384	161.74	8	631
BDMPM	256×256	-	22	259
PoolNet	384×384	68.26	17	410
GateNet	384×384	128.63	30	503
MINet	320×320	162.38	25	635
DSRNet	400×400	75.29	15	290
本文	320×320	29.97	26	117

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表 3 本文方法使用不同模块定量比较结果

方法	HKU-IS (4447)			DUTS-TE (5019)
方法	MAE	AF_m	WF_m	MAE	AF_m	WF_m
Res	0.042	0.866	0.843	0.053	0.772	0.760
Res+FPN	0.037	0.884	0.870	0.045	0.800	0.784
Res+DCIE+FPN	0.028	0.913	0.900	0.037	0.845	0.828
Res+DCIE+MFHI	0.026	0.923	0.911	0.034	0.862	0.846

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表 4 MFHI模块不同聚合策略定量比较结果

方法	HKU-IS (4447)			DUTS-TE (5019)
方法	MAE	AF_m	WF_m	MAE	AF_m	WF_m
Res+MFHI(cat)	0.030	0.903	0.892	0.041	0.822	0.813
Res+MFHI(mul)	0.032	0.899	0.888	0.041	0.818	0.811
Res+MFHI(add)	0.033	0.894	0.884	0.042	0.818	0.803
Res+MFHI(h1)	0.030	0.903	0.892	0.039	0.825	0.815
Res+MFHI(h2)	0.031	0.902	0.891	0.039	0.824	0.815
Res+MFHI	0.029	0.906	0.898	0.039	0.832	0.819

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表 5 不同模块对比测试

方法	HKU-IS (4447)			DUTS-TE (5019)
方法	MAE	AF_m	WF_m	MAE	AF_m	WF_m
Res+ASPP+FPN	0.031	0.905	0.892	0.040	0.825	0.815
Res+RFB+FPN	0.031	0.903	0.891	0.039	0.828	0.818
Res+PDC+FPN	0.032	0.899	0.899	0.040	0.826	0.813
Res+DCIE+FPN	0.028	0.913	0.900	0.037	0.845	0.828

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表 6 DCIE模块消融分析

方法	HKU-IS (4447)			DUTS-TE (5019)
方法	MAE	AF_m	WF_m	MAE	AF_m	WF_m
Res+DCIE(w D)+FPN	0.032	0.898	0.886	0.039	0.823	0.815
Res+DCIE(w A)+FPN	0.032	0.898	0.886	0.040	0.822	0.814
Res+DCIE(w D+A)+FPN	0.030	0.907	0.895	0.038	0.839	0.823
Res+DCIE+FPN	0.028	0.913	0.900	0.037	0.845	0.828

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