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ECW-EGNet: Exploring Cross-Modal Weighting and Edge-Guided Decoder Network for RGB-D Salient Object Detection
Computer Science and Information Systems, Tome 21 (2024) no. 3.

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Existing RGB-D salient object detection (SOD) techniques concentrate on combining data from multiple modalities (e.g., depth and RGB) and extracting multi-scale data for improved saliency reasoning. However, they frequently perform poorly as a factor of the drawbacks of low-quality depth maps and the lack of correlation between the extracted multi-scale data. In this paper, we propose a Exploring Cross-Modal Weighting and Edge-Guided Decoder Network (ECW-EGNet) for RGB-D SOD, which includes three prominent components. Firstly, we deploy a Cross-Modality Weighting Fusion (CMWF) module that utilizes Channel-Spatial Attention Feature Enhancement (CSAE) mechanism and Depth-Quality Assessment (DQA) mechanism to achieve the cross-modal feature interaction. The former parallels channel attention and spatial attention enhances the features of extracted RGB streams and depth streams while the latter assesses the depth-quality reduces the detrimental influence of the low-quality depth maps during the cross-modal fusion. Then, in order to effectively integrate multi-scale features for high-level and produce salient objects with precise locations, we construct a Bi-directional Scale-Correlation Convolution (BSCC) module in a bi-directional structure. Finally, we construct an Edge-Guided (EG) decoder that uses the edge detection operator to obtain edge masks to guide the enhancement of salient map edge details. The comprehensive experiments on five benchmark RGB-D SOD datasets demonstrate that the proposed ECW-EGNet outperforms 21 state-of-the-art (SOTA) saliency detectors in four widely used evaluation metrics.
Keywords: cross-modality fusion, depth-quality, edge-guided, RGB-D images, salient object detection 
@article{CSIS_2024_21_3_a12,
     author = {Chenxing Xia and Feng Yang and Songsong Duan and Xiuju Gao and Bin Ge and Kuan-Ching Li and Xianjin Fang and Yan Zhang and Ke Yang},
     title = {ECW-EGNet: {Exploring} {Cross-Modal} {Weighting} and {Edge-Guided} {Decoder} {Network} for {RGB-D} {Salient} {Object} {Detection}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {21},
     number = {3},
     year = {2024},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a12/}
}
TY  - JOUR
AU  - Chenxing Xia
AU  - Feng Yang
AU  - Songsong Duan
AU  - Xiuju Gao
AU  - Bin Ge
AU  - Kuan-Ching Li
AU  - Xianjin Fang
AU  - Yan Zhang
AU  - Ke Yang
TI  - ECW-EGNet: Exploring Cross-Modal Weighting and Edge-Guided Decoder Network for RGB-D Salient Object Detection
JO  - Computer Science and Information Systems
PY  - 2024
VL  - 21
IS  - 3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a12/
ID  - CSIS_2024_21_3_a12
ER  - 
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%A Chenxing Xia
%A Feng Yang
%A Songsong Duan
%A Xiuju Gao
%A Bin Ge
%A Kuan-Ching Li
%A Xianjin Fang
%A Yan Zhang
%A Ke Yang
%T ECW-EGNet: Exploring Cross-Modal Weighting and Edge-Guided Decoder Network for RGB-D Salient Object Detection
%J Computer Science and Information Systems
%D 2024
%V 21
%N 3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a12/
%F CSIS_2024_21_3_a12
Chenxing Xia; Feng Yang; Songsong Duan; Xiuju Gao; Bin Ge; Kuan-Ching Li; Xianjin Fang; Yan Zhang; Ke Yang. ECW-EGNet: Exploring Cross-Modal Weighting and Edge-Guided Decoder Network for RGB-D Salient Object Detection. Computer Science and Information Systems, Tome 21 (2024) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a12/