面向360度全景图像显著目标检测的相邻协调网络

陈晓雷; 王兴; 张学功; 杜泽龙

doi:10.11999/JEIT240502

面向360度全景图像显著目标检测的相邻协调网络

doi: 10.11999/JEIT240502

兰州理工大学电气工程与信息工程学院兰州 730050

基金项目: 国家自然科学基金 (61967012)

详细信息

作者简介:
陈晓雷：男，博士，副教授，研究方向为人工智能、计算机视觉、虚拟现实

王兴：男，硕士生，研究方向为虚拟现实、360°全景图像显著目标检测

张学功：男，硕士生，研究方向为显著目标检测、人工智能

杜泽龙：男，硕士生，研究方向为图像处理、虚拟现实

通讯作者:
陈晓雷　chenxl703@lut.edu.cn

中图分类号: TN911; TP391
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- 文章访问数: 51
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- PDF下载量: 7
- 被引次数: 0
出版历程
- 收稿日期: 2024-06-19
- 修回日期: 2024-11-15
- 网络出版日期: 2024-11-27

Adjacent Coordination Network for Salient Object Detection in 360 Degree Omnidirectional Images

College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China

Funds: The National Natural Science Foundation of China (61967012)

摘要

摘要: 为解决360°全景图像显著目标检测(SOD)中的显著目标尺度变化和边缘不连续、易模糊的问题，该文提出一种基于相邻协调网络的360°全景图像显著目标检测方法(ACoNet)。首先，利用相邻细节融合模块获取相邻特征中的细节和边缘信息，以促进显著目标的精确定位。其次，使用语义引导特征聚合模块来聚合浅层特征和深层特征之间不同尺度上的语义特征信息，并抑制浅层特征传递的噪声，缓解解码阶段显著目标与背景区域不连续、边界易模糊的问题。同时构建多尺度语义融合子模块扩大不同卷积层的多尺度感受野，实现精确训练显著目标边界的效果。在2个公开的数据集上进行的大量实验结果表明，相比于其他13种先进方法，所提方法在6个客观评价指标上均有明显的提升，同时主观可视化检测的显著图边缘轮廓性更好，空间结构细节信息更清晰。
- 显著目标检测 /
- 深度学习 /
- 360°全景图像 /
- 多尺度特征
Abstract: To address the issues of significant target scale variation, edge discontinuity, and blurring in 360° omnidirectional images Salient Object Detection (SOD), a method based on the Adjacent Coordination Network (ACoNet) is proposed. First, an adjacent detail fusion module is used to capture detailed and edge information from adjacent features, which facilitates accurate localization of salient objects. Then, a semantic-guided feature aggregation module is employed to aggregate semantic feature information from different scales between shallow and deep features, suppressing the noise transmitted by shallow features. This helps alleviate the problem of discontinuous salient objects and blurred boundaries between the object and background in the decoding stage. Additionally, a multi-scale semantic fusion submodule is constructed to enlarge the receptive field across different convolution layers, thereby achieving better training of the salient object boundaries. Extensive experimental results on two public datasets demonstrate that, compared to 13 other advanced methods, the proposed approach achieves significant improvements in six objective evaluation metrics. Moreover, the subjective visualized detection results show better edge contours and clearer spatial structural details of the salient maps.
- Salient Object Detection (SOD) /
- Deep learning /
- 360° omnidirectional images /
- Multi-scale features

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图 1 ACoNet网络整体结构图

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图 2 相邻细节融合模块ADFM结构图

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图 3 语义引导特征聚合模块SGFA结构图

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图 4 多尺度语义融合子模块结构图

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图 5 360-SOD数据集上不同模型的主观可视化对比结果

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图 6 360-SSOD数据集上不同模型的主观可视化对比结果

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图 7 本文模型与不同先进方法损失函数收敛对比结果

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图 8 相关模块以及结构的消融实验可视化对比

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表 1 360-SOD 数据集上不同方法客观指标对比

		MAE↓	max-F↑	mean-F↑	max-Em↑	mean-Em↑	Sm↑
360°SOD	本文方法	0.0181	0.7893	0.7815	0.9141	0.9043	0.8493
	MPFRNet(2023)	0.0190	0.7653	0.7556	0.8854	0.8750	0.8416
	LDNet(2023)	0.0289	0.6561	0.6414	0.8655	0.8437	0.7680
	FANet(2020)	0.0208	0.7699	0.7485	0.9002	0.8729	0.8261
2D SOD	MEANet(2023)	0.0243	0.7098	0.7016	0.8675	0.8398	0.7808
	BSCGNet(2023)	0.0246	0.6795	0.6725	0.8666	0.8380	0.7844
	SeaNet(2023)	0.0293	0.6080	0.5981	0.8611	0.8319	0.7374
	AGNet(2022)	0.0221	0.7503	0.7410	0.8754	0.8574	0.8055
	ACCoNet(res)(2022)	0.0243	0.6855	0.6735	0.8690	0.8159	0.7864
	ACCoNet(vgg)(2022)	0.0245	0.6910	0.6782	0.8629	0.8060	0.7711
	MSCNet(2022)	0.0247	0.7286	0.7162	0.8740	0.8672	0.8139
	CorrNet(2022)	0.0474	0.2515	0.1673	0.5465	0.3867	0.5322
	PGNet(2022)	0.0237	0.6944	0.6861	0.8625	0.8360	0.7895
	CSDF(2022)	0.0275	0.6621	0.6433	0.8521	0.7829	0.7530
	SwinNet(2021)	0.0240	0.7023	0.6818	0.8624	0.8315	0.7897

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表 2 360-SSOD 数据集上不同方法客观指标对比

		MAE↓	max-F↑	mean-F↑	max-Em↑	mean-Em↑	Sm↑
360°SOD	本文方法	0.0288	0.6641	0.6564	0.8695	0.8632	0.7796
	MPFRNet(2023)	---	---	---	---	---	---
	LDNet(2023)	0.0341	0.5868	0.5695	0.8415	0.8221	0.7252
	FANet(2020)	0.0421	0.5939	0.5215	0.8426	0.7324	0.7186
2D SOD	MEANet(2023)	0.0289	0.6343	0.6242	0.8578	0.8437	0.7479
	BSCGNet(2023)	0.0316	0.5969	0.5832	0.8216	0.8079	0.7395
	SeaNet(2023)	0.0383	0.3929	0.3432	0.7525	0.5695	0.5978
	AGNet(2022)	0.0297	0.6371	0.6291	0.8409	0.8176	0.7701
	ACCoNet(res)(2022)	0.0365	0.5752	0.5555	0.8312	0.7929	0.7341
	ACCoNet(vgg)(2022)	0.0312	0.6043	0.5904	0.8297	0.7938	0.7358
	MSCNet(2022)	0.0370	0.6239	0.6026	0.8512	0.8328	0.7613
	CorrNet(2022)	0.0540	0.2586	0.0904	0.7046	0.3347	0.5006
	PGNet(2022)	0.0946	0.0772	0.0545	0.5889	0.5613	0.4493
	CSDF(2022)	0.0326	0.5490	0.5211	0.8117	0.7399	0.6942
	SwinNet(2021)	0.0940	0.0772	0.0469	0.5877	0.5379	0.4482

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表 3 本文模型与不同先进方法复杂度对比结果

复杂度指标	本文模型	FANet	MEANet	BSCGNet	SeaNet	AGNet
GFLOPs(G)	76.24	340.94	11.99	86.46	2.86	25.28
Params(M)	24.15	25.40	3.27	36.99	2.75	24.55

复杂度指标	ACCoNet	MSCNet	CorrNet	PGNet	CSDF	SwinNet
GFLOPs(G)	406.06	15.47	42.63	14.37	78.14	43.56
Params(M)	127.00	3.26	4.07	72.67	26.24	97.56

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表 4 相关模块以及多分支结构的消融实验结果

Baseline	ADFM	SGFA	多分支结构	MAE↓	max-F↑	mean-F↑	max-Em↑	mean-Em↑	Sm↑
√				0.0238	0.7193	0.7107	0.8823	0.8525	0.7912
√	√			0.0220	0.7580	0.7466	0.9133	0.8898	0.8185
√		√		0.0209	0.7562	0.7517	0.8915	0.8796	0.8252
√			√	0.0206	0.7584	0.7483	0.8959	0.8745	0.8304
√	√	√		0.0207	0.7819	0.7642	0.9170	0.8650	0.8244
√	√		√	0.0202	0.7704	0.7587	0.9007	0.8899	0.8322
√		√	√	0.0184	0.7777	0.7660	0.9113	0.8878	0.8407
√	√	√	√	0.0181	0.7893	0.7815	0.9141	0.9043	0.8493

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