RGB-T Salient Object Detection via Fusing Multi-Level CNN Features
Abstract
RGB-induced salient object detection has recently witnessed substantial progress, which is attributed to the superior feature learning capability of deep convolutional neural networks (CNNs). However, such detections suffer from challenging scenarios characterized by cluttered backgrounds, low-light conditions and variations in illumination. Instead of improving RGB based saliency detection, this paper takes advantage of the complementary benefits of RGB and thermal infrared images. Specifically, we propose a novel end-to-end network for multi-modal salient object detection, which turns the challenge of RGB-T saliency detection to a CNN feature fusion problem. To this end, a backbone network (e.g., VGG-16) is first adopted to extract the coarse features from each RGB or thermal infrared image individually, and then several adjacent-depth feature combination (ADFC) modules are designed to extract multi-level refined features for each single-modal input image, considering that features captured at different depths differ in semantic information and visual details. Subsequently, a multi-branch group fusion (MGF) module is employed to capture the cross-modal features by fusing those features from ADFC modules for a RGB-T image pair at each level. Finally, a joint attention guided bi-directional message passing (JABMP) module undertakes the task of saliency prediction via integrating the multi-level fused features from MGF modules. Experimental results on several public RGB-T salient object detection datasets demonstrate the superiorities of our proposed algorithm over the state-of-the-art approaches, especially under challenging conditions, such as poor illumination, complex background and low contrast.
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A multi-branch group fusion module is proposed to fuse features from RGB and thermal infrared images at different levels in [182], since semantic information and visual details differ at different levels.
“For example, a multi-branch group fusion module is proposed to fuse features from RGB and thermal infrared images at different levels in [182], since the semantic information and visual details differ at different levels.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [182]
By using them with RGB images as input, cross-modal perceiving has attracted increasing attention in real-world applications, e.g., scene parsing for autonomous driving [181], [85], object detection and tracking in low-light scenarios [182], [183], and action recognition [184].
“By using them with RGB images as input, cross-modal perceiving has attracted increasing attention in real-world applications, e.g., scene parsing for autonomous driving [181], [85], object detection and tracking in low-light scenarios [182], [183], and action recognition [184].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [182]
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Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
RGB-D salient object detection: A survey
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- Authors
- Zhang, Q., Huang, N., Yao, L., Zhang, D., Shan, C., & Han, J.
- Affiliations
- Xidian University (CN)
- Philips (Netherlands) (NL)
- University of Warwick (GB)
- Venue
- IEEE Transactions on Image Processing
- Published
- 2020
- Volume
- 29
- Pages
- 3321-3335
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2995936506
- Semantic Scholar
- 4c43a52e4b9e780e7dbe072ee5d08a486bd4f163
- Cited by
- 247
- References
- 77
- Citations by year
- 2026: 23 · 2025: 37 · 2024: 37 · 2023: 52 · 2022: 49 · 2021: 38 · 2020: 11
- FWCI
- 10.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Image Processing (publishedVersion)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Funders
- National Natural Science Foundation of China (61773301, 61876140, 61971004)
- China Postdoctoral Support Scheme for Innovative Talents (BX20180236)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Engineering › Media Technology › Advanced Image Fusion Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- thermal infrared images, RGB-thermal fusion, multi-level feature fusion, cross-modal feature fusion, background clutter, low-light conditions, illumination variation
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