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However, optimizing the fusion of multi-modal and multi-scale features to enhance detection performance remains a challenge. To address this issue, we propose a network model based on semantic localization and multi-scale fusion (SLMSF-Net), specifically designed for RGB-D SOD. Firstly, we designed a Deep Attention Module (DAM), which extracts valuable depth feature information from both channel and spatial perspectives and efficiently merges it with RGB features. Subsequently, a Semantic Localization Module (SLM) is introduced to enhance the top-level modality fusion features, enabling the precise localization of salient objects. Finally, a Multi-Scale Fusion Module (MSF) is employed to perform inverse decoding on the modality fusion features, thus restoring the detailed information of the objects and generating high-precision saliency maps. Our approach has been validated across six RGB-D salient object detection datasets. The experimental results indicate an improvement of 0.20~1.80%, 0.09~1.46%, 0.19~1.05%, and 0.0002~0.0062, respectively in maxF, maxE, S, and MAE metrics, compared to the best competing methods (AFNet, DCMF, and C2DFNet).<\/jats:p>","DOI":"10.3390\/s24041117","type":"journal-article","created":{"date-parts":[[2024,2,8]],"date-time":"2024-02-08T09:00:02Z","timestamp":1707382802000},"page":"1117","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["SLMSF-Net: A Semantic Localization and Multi-Scale Fusion Network for RGB-D Salient Object Detection"],"prefix":"10.3390","volume":"24","author":[{"given":"Yanbin","family":"Peng","sequence":"first","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhinian","family":"Zhai","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingkun","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1109\/TPAMI.2021.3140168","article-title":"Poolnet+: Exploring the potential of pooling for salient object detection","volume":"45","author":"Liu","year":"2022","journal-title":"IEEE Trans. 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