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Deep Learning for Information Fusion and Pattern Recognition—Second Edition
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Deep Learning for Information Fusion and Pattern Recognition—Second Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".

Deadline for manuscript submissions: 10 November 2025 | Viewed by 350

Special Issue Editors

Dr. Yufeng Zheng

E-Mail Website
Guest Editor
Department of Data Science, University of Mississippi Medical Center, Jackson, MS, USA
Interests: image processing; deep learning; computer vision; computer-aided detection/diagnosis
Special Issues, Collections and Topics in MDPI journals
Dr. Erik Blasch

E-Mail Website
Guest Editor
Air Force Office of Scientific Research, Arlington, VA 22203-1768, USA
Interests: information fusion; space-aware tracking; industrial avionics; human factors
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the previous Special Issue “Deep Learning for Information Fusion and Pattern Recognition”, we are pleased to announce the next in the series, entitled “Deep Learning for Information Fusion and Pattern Recognition—Second Edition”.

There are a large amount of data from different types of sensors, for instance, multispectral electro-optical/infrared (EO/IR) and computed tomography/magnetic resonance (CT/MR) images. Determining how to take advantage of multimodal data for object detection and pattern recognition is an active field of research. Information fusion (IF) is a venue to enhance the performance of pattern classification, and deep learning (DL) technologies, including convolutional neural networks (CNNs), are powerful tools to improve object detection, segmentation, and recognition. It is viable to combine DL and IF to boost the overall performance of pattern classification and target recognition. Such combinations of powerful techniques may exploit the deeply hidden features from multimodal, spatial, or temporal data. Example applications may include (but are not limited to) sensor fusion, face recognition, cancer detection, image fusion, object detection, target recognition, robot vision, autonomous driving, and AI image/video generators.

Dr. Yufeng Zheng
Dr. Erik Blasch
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensor fusion
  • face recognition
  • cancer detection
  • image fusion
  • object detection
  • target recognition
  • robot vision
  • autonomous driving
  • AI image/video generators

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Published Papers (1 paper)

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Research

24 pages, 2629 KiB  
Article
Robust Infrared–Visible Fusion Imaging with Decoupled Semantic Segmentation Network
by Xuhui Zhang, Yunpeng Yin, Zhuowei Wang, Heng Wu, Lianglun Cheng, Aimin Yang and Genping Zhao
Sensors 2025, 25(9), 2646; https://doi.org/10.3390/s25092646 - 22 Apr 2025
Viewed by 224
Abstract
The fusion of infrared and visible images provides complementary information from both modalities and has been widely used in surveillance, military, and other fields. However, most of the available fusion methods have only been evaluated with subjective metrics of visual quality of the [...] Read more.
The fusion of infrared and visible images provides complementary information from both modalities and has been widely used in surveillance, military, and other fields. However, most of the available fusion methods have only been evaluated with subjective metrics of visual quality of the fused images, which are often independent of the following relevant high-level visual tasks. Moreover, as a useful technique especially used in low-light scenarios, the effect of low-light conditions on the fusion result has not been well-addressed yet. To address these challenges, a decoupled and semantic segmentation-driven infrared and visible image fusion network is proposed in this paper, which connects both image fusion and the downstream task to drive the network to be optimized. Firstly, a cross-modality transformer fusion module is designed to learn rich hierarchical feature representations. Secondly, a semantic-driven fusion module is developed to enhance the key features of prominent targets. Thirdly, a weighted fusion strategy is adopted to automatically adjust the fusion weights of different modality features. This effectively merges the thermal characteristics from infrared images and detailed information from visible images. Additionally, we design a refined loss function that employs the decoupling network to constrain the pixel distributions in the fused images and produce more-natural fusion images. To evaluate the robustness and generalization of the proposed method in practical challenge applications, a Maritime Infrared and Visible (MIV) dataset is created and verified for maritime environmental perception, which will be made available soon. The experimental results from both widely used public datasets and the practically collected MIV dataset highlight the notable strengths of the proposed method with the best-ranking quality metrics among its counterparts. Of more importance, the fusion image achieved with the proposed method has over 96% target detection accuracy and a dominant high mAP@[50:95] value that far surpasses all the competitors. Full article
(This article belongs to the Special Issue Deep Learning for Information Fusion and Pattern Recognition—Second Edition)
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