RGB-D Saliency Object Detection Based on Cross-refinement and Circular Attention

doi:10.16182/j.issn1004731x.joss.22-1372

Abstract

Abstract:

In order to solve the problems that the boundary of the saliency object detection area is vague, and the detection area is incomplete or inaccurate, an RGB-D saliency object detection method based on cross-refinement and circular attention is proposed. A cross-refinement module is designed at the stage of extracting features using encoders, which is used to supplement feature information of each other and improve the feature quality before fusion. It also suppresses the negative impact of poor-quality depth maps and addresses the issue that the edges of the saliency object are blurred. For the features after fusion, the circular module is proposed, which combines the attention mechanism with convolutional long short-term memory (LSTM) network unit to simulate the internal generation mechanism of the brain and help infer the current decision by retrieving past memories, so as to obtain semantic scenes that require long-term memory. The module can comprehensively learn the internal semantic relationships of fusion features to generate a more complete and accurate saliency map for the detection area. Experiments conducted on six public datasets show that the proposed method can obtain a saliency map with clear edges and high accuracy.

Key words: RGB-D, saliency object detection, cross-refinement, attention mechanism, convolutional long short-term memory network, circular module

CLC Number:

TP391.9

Dong Qingqing, Wu Hao, Qian Wenhua, Kong Fengling. RGB-D Saliency Object Detection Based on Cross-refinement and Circular Attention[J]. Journal of System Simulation, 2023, 35(9): 1931-1947.

Figures/Tables 16

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Table 1

Quantitative comparison of proposed method with 11 models in terms of six indicators

数据集	指标	JLDCF	HAINet	CDNet	DSA^2F	JSM	Mobile Sal	DSU	CFIDnet	DCMF	SSL	C²DFnet	Ours
DES	$M A E ↓$	0.021	0.018	0.020	0.023	0.058	0.021	0.063	0.023	0.023	0.019	0.020	0.016
	$F β m a x ↑$	0.923	0.936	0.936	0.916	0.808	0.924	0.797	0.911	0.924	0.936	0.916	0.937
	$F β a v g ↑$	0.907	0.921	0.899	0.904	0.762	0.908	0.753	0.898	0.899	0.919	0.907	0.922
	$F β ω ↑$	0.889	0.910	0.863	0.889	0.742	0.896	0.682	0.886	0.879	0.908	0.898	0.910
	$s α ↑$	0.931	0.931	0.931	0.917	0.820	0.929	0.818	0.917	0.932	0.932	0.922	0.933
	$E ξ ↑$	0.968	0.971	0.971	0.954	0.875	0.970	0.888	0.940	0.968	0.972	0.959	0.973
LFSD	$M A E ↓$	0.070	0.079	0.088	0.055	0.127	0.080	0.129	0.071	0.069	0.079	0.065	0.070
	$F β m a x ↑$	0.867	0.853	0.841	0.889	0.781	0.841	0.792	0.865	0.875	0.835	0.867	0.869
	$F β a v g ↑$	0.848	0.842	0.822	0.879	0.757	0.825	0.759	0.850	0.847	0.826	0.859	0.859
	$F β ω ↑$	0.805	0.803	0.775	0.848	0.724	0.780	0.710	0.807	0.806	0.791	0.829	0.826
	$s α ↑$	0.861	0.854	0.842	0.883	0.767	0.847	0.778	0.869	0.877	0.844	0.864	0.878
	$E ξ ↑$	0.902	0.886	0.877	0.924	0.833	0.888	0.842	0.903	0.909	0.879	0.903	0.913
NJU2K	$M A E ↓$	0.036	0.038	0.060	0.040	0.133	0.047	0.135	0.038	0.051	0.043	0.035	0.029
	$F β m a x ↑$	0.915	0.915	0.871	0.907	0.710	0.896	0.720	0.915	0.873	0.902	0.919	0.926
	$F β a v g ↑$	0.902	0.903	0.847	0.898	0.673	0.875	0.683	0.899	0.868	0.887	0.897	0.916
	$F β ω ↑$	0.880	0.878	0.804	0.878	0.647	0.840	0.631	0.871	0.844	0.861	0.878	0.898
	$s α ↑$	0.912	0.912	0.876	0.904	0.713	0.895	0.727	0.914	0.871	0.901	0.908	0.925
	$E ξ ↑$	0.950	0.944	0.911	0.938	0.801	0.932	0.805	0.946	0.915	0.938	0.943	0.956
SIP	$M A E ↓$	0.049	0.053	0.059	0.057	0.146	0.053	0.156	0.060	0.043	0.058	0.053	0.043
	$F β m a x ↑$	0.889	0.892	0.878	0.875	0.645	0.880	0.624	0.870	0.899	0.874	0.877	0.902
	$F β a v g ↑$	0.873	0.876	0.848	0.866	0.622	0.862	0.605	0.857	0.891	0.862	0.864	0.889
	$F β ω ↑$	0.845	0.845	0.805	0.840	0.562	0.834	0.520	0.829	0.872	0.841	0.834	0.866
	$s α ↑$	0.880	0.880	0.870	0.862	0.697	0.873	0.687	0.864	0.886	0.868	0.872	0.892
	$E ξ ↑$	0.924	0.922	0.914	0.912	0.774	0.916	0.766	0.909	0.930	0.910	0.916	0.931
NLPR^]	$M A E ↓$	0.023	0.024	0.062	0.024	0.060	0.025	0.065	0.026	0.029	0.027	0.022	0.021
	$F β m a x ↑$	0.917	0.915	0.810	0.906	0.771	0.908	0.765	0.905	0.906	0.899	0.918	0.924
	$F β a v g ↑$	0.894	0.901	0.757	0.896	0.743	0.886	0.734	0.892	0.872	0.882	0.903	0.910
	$F β ω ↑$	0.866	0.877	0.684	0.873	0.706	0.858	0.652	0.867	0.836	0.859	0.882	0.890
	$s α ↑$	0.925	0.924	0.850	0.919	0.805	0.920	0.807	0.922	0.922	0.913	0.928	0.931
	$E ξ ↑$	0.963	0.960	0.892	0.952	0.875	0.961	0.875	0.955	0.954	0.949	0.963	0.965
STERE	$M A E ↓$	0.040	0.040	0.059	0.039	0.096	0.041	0.099	0.043	0.043	0.047	0.039	0.037
	$F β m a x ↑$	0.904	0.906	0.870	0.900	0.772	0.895	0.779	0.897	0.906	0.883	0.897	0.907
	$F β a v g ↑$	0.874	0.887	0.831	0.889	0.747	0.875	0.752	0.887	0.869	0.867	0.881	0.891
	$F β ω ↑$	0.831	0.852	0.769	0.866	0.709	0.842	0.689	0.850	0.825	0.837	0.849	0.860
	$s α ↑$	0.903	0.907	0.871	0.898	0.783	0.903	0.789	0.901	0.910	0.885	0.902	0.908
	$E ξ ↑$	0.945	0.944	0.922	0.942	0.852	0.940	0.861	0.942	0.946	0.930	0.943	0.947

Table 1

Fig. 11

Table 2

Quantitative ablation experimental results of proposed method

模型	SIP				NLPR				STERE
模型	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$
Baseline	0.044	0.889	0.878	0.918	0.023	0.915	0.922	0.960	0.039	0.893	0.896	0.938
+MCR	0.043	0.899	0.886	0.927	0.022	0.919	0.928	0.962	0.038	0.900	0.903	0.942
+RCL	0.043	0.900	0.889	0.929	0.022	0.921	0.927	0.963	0.038	0.902	0.904	0.940
Ours	0.043	0.902	0.892	0.931	0.021	0.924	0.931	0.965	0.037	0.907	0.908	0.947

Table 2

Table 3

Ablation analysis of multi-modal cross-refinement strategy in MCR modules

不同策略的MCR	SIP				NLPR				STERE
不同策略的MCR	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$
Cat	0.044	0.898	0.891	0.931	0.022	0.924	0.929	0.963	0.037	0.906	0.908	0.946
Mul	0.044	0.899	0.890	0.930	0.022	0.923	0.927	0.961	0.038	0.905	0.906	0.943
Mul+Cat	0.045	0.895	0.889	0.929	0.022	0.922	0.926	0.962	0.038	0.904	0.905	0.942
Ours	0.043	0.902	0.892	0.931	0.021	0.924	0.931	0.965	0.037	0.907	0.908	0.947

Table 3

Table 4

Effectiveness analysis of backbone networks

骨干网络	SIP				NLPR				STERE
骨干网络	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$
VGG11	0.047	0.879	0.869	0.882	0.027	0.909	0.911	0.955	0.043	0.873	0.876	0.918
VGG16	0.045	0.882	0.872	0.898	0.025	0.915	0.918	0.957	0.041	0.881	0.882	0.922
ResNet34	0.044	0.899	0.890	0.925	0.024	0.922	0.929	0.960	0.039	0.899	0.900	0.939
ResNet50	0.043	0.902	0.892	0.931	0.021	0.924	0.931	0.965	0.037	0.907	0.908	0.947

Table 4

Table 5

Quantitative comparison of proposed method and original models

模型	HAINet				C²DFNet^[46]
模型	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$	$M A E ↓$	$F β m a x ↑$	$s α ↑$	$E ξ ↑$
Baseline	0.038	0.915	0.912	0.944	0.035	0.919	0.908	0.943
+MCR	0.034	0.920	0.919	0.948	0.031	0.923	0.917	0.953
+RCL	0.033	0.921	0.922	0.948	0.033	0.921	0.915	0.950

Table 5

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