Article: MECNet: multi-modal edge co-guidance network for RGB-D salient object detection Journal: International Journal of Computational Science and Engineering (IJCSE) 2025 Vol.28 No.4 pp.458 - 470 Abstract: Currently, mainstream RGB-D salient object detection (SOD) methods rely on depth information to supplement RGB information, which may suffer from inappropriate results due to simplistic fusion. Furthermore, numerous existing methods mainly yield saliency maps with erroneous or blurry edges because of their inadequacy in harnessing edge details and local information from RGB and depth images. Therefore, we propose a multi-modal edge co-guidance network (MECNet) for RGB-D SOD. Firstly, a multimodal attention fusion module (MAFM) is designed to fuse RGB and depth information effectively. Moreover, an edge co-guidance module (ECM) which uses edge consistency between RGB images and depth maps to capture the coherence edge information is developed. In the decoding process, a level-by-level cascade fusion is performed through simple element-wise addition. Finally, the coherence edge information is integrated into the saliency output stage to generate clear and sharp saliency maps. Extensive experiments illustrate the effectiveness and generalisability of our method. Inderscience Publishers - linking academia, business and industry through research

Title: MECNet: multi-modal edge co-guidance network for RGB-D salient object detection

Authors: Xiuju Gao; Chenxing Xia; Xia Chen; Jianhua Cui

Addresses: The First Affiliated Hospital of Anhui University of Science and Technology (Huainan First People's Hospital), Huainan, Anhui, China ' The First Affiliated Hospital of Anhui University of Science and Technology (Huainan First People's Hospital), Huainan, Anhui, China ' Anyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Anyang, Henan, China ' Anyang Cigarette Factory, China Tobacco Henan Industrial Co., Ltd., Anyang, Henan, China

Abstract: Currently, mainstream RGB-D salient object detection (SOD) methods rely on depth information to supplement RGB information, which may suffer from inappropriate results due to simplistic fusion. Furthermore, numerous existing methods mainly yield saliency maps with erroneous or blurry edges because of their inadequacy in harnessing edge details and local information from RGB and depth images. Therefore, we propose a multi-modal edge co-guidance network (MECNet) for RGB-D SOD. Firstly, a multimodal attention fusion module (MAFM) is designed to fuse RGB and depth information effectively. Moreover, an edge co-guidance module (ECM) which uses edge consistency between RGB images and depth maps to capture the coherence edge information is developed. In the decoding process, a level-by-level cascade fusion is performed through simple element-wise addition. Finally, the coherence edge information is integrated into the saliency output stage to generate clear and sharp saliency maps. Extensive experiments illustrate the effectiveness and generalisability of our method.

Keywords: coherence edge; multimodal fusion; RGB and depth images; salient object detection; SOD.

DOI: 10.1504/IJCSE.2025.147648

International Journal of Computational Science and Engineering, 2025 Vol.28 No.4, pp.458 - 470

Received: 27 Apr 2024
Accepted: 03 Jul 2024
Published online: 24 Jul 2025 *

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Title: MECNet: multi-modal edge co-guidance network for RGB-D salient object detection

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