面向360度全景圖像顯著目標(biāo)檢測的相鄰協(xié)調(diào)網(wǎng)絡(luò)

陳曉雷; 王興; 張學(xué)功; 杜澤龍

doi:10.11999/JEIT240502

面向360度全景圖像顯著目標(biāo)檢測的相鄰協(xié)調(diào)網(wǎng)絡(luò)

doi: 10.11999/JEIT240502 cstr: 32379.14.JEIT240502

蘭州理工大學(xué)電氣工程與信息工程學(xué)院蘭州 730050

基金項目: 國家自然科學(xué)基金 (61967012)

詳細(xì)信息

作者簡介:
陳曉雷：男，博士，副教授，研究方向為人工智能、計算機(jī)視覺、虛擬現(xiàn)實

王興：男，碩士生，研究方向為虛擬現(xiàn)實、360°全景圖像顯著目標(biāo)檢測

張學(xué)功：男，碩士生，研究方向為顯著目標(biāo)檢測、人工智能

杜澤龍：男，碩士生，研究方向為圖像處理、虛擬現(xiàn)實

通訊作者:
陳曉雷　chenxl703@lut.edu.cn

中圖分類號: TN911; TP391
計量
- 文章訪問數(shù): 169
- HTML全文瀏覽量: 107
- PDF下載量: 34
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2024-06-19
- 修回日期: 2024-11-15
- 網(wǎng)絡(luò)出版日期: 2024-11-27
- 刊出日期: 2024-12-01

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: 為解決360°全景圖像顯著目標(biāo)檢測(SOD)中的顯著目標(biāo)尺度變化和邊緣不連續(xù)、易模糊的問題，該文提出一種基于相鄰協(xié)調(diào)網(wǎng)絡(luò)的360°全景圖像顯著目標(biāo)檢測方法(ACoNet)。首先，利用相鄰細(xì)節(jié)融合模塊獲取相鄰特征中的細(xì)節(jié)和邊緣信息，以促進(jìn)顯著目標(biāo)的精確定位。其次，使用語義引導(dǎo)特征聚合模塊來聚合淺層特征和深層特征之間不同尺度上的語義特征信息，并抑制淺層特征傳遞的噪聲，緩解解碼階段顯著目標(biāo)與背景區(qū)域不連續(xù)、邊界易模糊的問題。同時構(gòu)建多尺度語義融合子模塊擴(kuò)大不同卷積層的多尺度感受野，實現(xiàn)精確訓(xùn)練顯著目標(biāo)邊界的效果。在2個公開的數(shù)據(jù)集上進(jìn)行的大量實驗結(jié)果表明，相比于其他13種先進(jìn)方法，所提方法在6個客觀評價指標(biāo)上均有明顯的提升，同時主觀可視化檢測的顯著圖邊緣輪廓性更好，空間結(jié)構(gòu)細(xì)節(jié)信息更清晰。
- 顯著目標(biāo)檢測 /
- 深度學(xué)習(xí) /
- 360°全景圖像 /
- 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

HTML全文

圖 1 ACoNet網(wǎng)絡(luò)整體結(jié)構(gòu)圖

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圖 2 相鄰細(xì)節(jié)融合模塊ADFM結(jié)構(gòu)圖

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圖 3 語義引導(dǎo)特征聚合模塊SGFA結(jié)構(gòu)圖

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圖 4 多尺度語義融合子模塊結(jié)構(gòu)圖

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圖 5 360-SOD數(shù)據(jù)集上不同模型的主觀可視化對比結(jié)果

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圖 6 360-SSOD數(shù)據(jù)集上不同模型的主觀可視化對比結(jié)果

下載: 全尺寸圖片幻燈片

圖 7 本文模型與不同先進(jìn)方法損失函數(shù)收斂對比結(jié)果

下載: 全尺寸圖片幻燈片

圖 8 相關(guān)模塊以及結(jié)構(gòu)的消融實驗可視化對比

下載: 全尺寸圖片幻燈片

表 1 360-SOD 數(shù)據(jù)集上不同方法客觀指標(biāo)對比

		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(2024)	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(2022)	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)(2023)	0.0243	0.6855	0.6735	0.8690	0.8159	0.7864
	ACCoNet(vgg)(2023)	0.0245	0.6910	0.6782	0.8629	0.8060	0.7711
	MSCNet(2023)	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(2021)	0.0237	0.6944	0.6861	0.8625	0.8360	0.7895
	CSDF(2023)	0.0275	0.6621	0.6433	0.8521	0.7829	0.7530
	SwinNet(2022)	0.0240	0.7023	0.6818	0.8624	0.8315	0.7897

下載: 導(dǎo)出CSV

表 2 360-SSOD 數(shù)據(jù)集上不同方法客觀指標(biāo)對比

		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(2024)	---	---	---	---	---	---
	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(2022)	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)(2023)	0.0365	0.5752	0.5555	0.8312	0.7929	0.7341
	ACCoNet(vgg)(2023)	0.0312	0.6043	0.5904	0.8297	0.7938	0.7358
	MSCNet(2023)	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(2021)	0.0946	0.0772	0.0545	0.5889	0.5613	0.4493
	CSDF(2023)	0.0326	0.5490	0.5211	0.8117	0.7399	0.6942
	SwinNet(2022)	0.0940	0.0772	0.0469	0.5877	0.5379	0.4482

下載: 導(dǎo)出CSV

表 3 本文模型與不同先進(jìn)方法復(fù)雜度對比結(jié)果

復(fù)雜度指標(biāo)	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

復(fù)雜度指標(biāo)	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

下載: 導(dǎo)出CSV

表 4 相關(guān)模塊以及多分支結(jié)構(gòu)的消融實驗結(jié)果

Baseline	ADFM	SGFA	多分支結(jié)構(gòu)	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

下載: 導(dǎo)出CSV

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