Topic Editors

Prof. Dr. Fengping An
School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
Department of Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Dr. Chuyang Ye
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100811, China

Transformer and Deep Learning Applications in Image Processing

Abstract submission deadline
31 March 2026
Manuscript submission deadline
31 May 2026
Viewed by
760

Topic Information

Dear Colleagues,

Convolutional Neural Networks (CNNs) represent a class of deep learning architectures specifically designed to process spatial data, such as images and videos. Due to their ability to autonomously extract features, maintain translational invariance, and perceive local patterns, CNNs have found extensive applications in domains such as image classification, object recognition, object tracking, and medical image processing. However, CNNs are unable to model long-range dependencies effectively and struggle to extract long-distance feature information of the target to be tracked, which impacts the efficiency and accuracy of target tracking. Since the release of ChatGPT 3.0 based on transformers on June 11, 2020, transformers have demonstrated strong capabilities in handling sequential data.

Although CNN models have achieved significant success in the field of image processing imagery over the years, many challenges remain in practical applications, such as complex scene image classification, specific object recognition and tracking, and medical image processing. This situation highlights a noticeable gap between theoretical advancements and practical applications in the image processing field.

Therefore, we invite submissions of studies on theoretical research and practical applications related to transformer and deep learning architectures in the fields of medical image analysis, image classification, recognition, and tracking.

We welcome submissions on the following topics, including but not limited to the following:

  • Novel architectures and variations of transformers and deep learning models;
  • Fine-tuning strategies for pre-trained transformers and deep learning models;
  • Image classification based on transformers and deep learning models;
  • Image recognition based on transformers and deep learning models;
  • Medical image processing based on transformers and deep learning models;
  • Object tracking based on transformers and deep learning models;
  • Transformers and deep learning for sciences;
  • Transformers and convolutional neural network fusion architecture;
  • Transformers and deep learning for diverse machine learning tasks;
  • Natural language processing.

Prof. Dr. Fengping An
Prof. Dr. Haitao Xu
Dr. Chuyang Ye
Topic Editors

Keywords

  • image processing
  • medical image processing
  • transformer
  • deep learning
  • CNN

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Diagnostics
diagnostics
3.0 4.7 2011 20.3 Days CHF 2600 Submit
Electronics
electronics
2.6 5.3 2012 16.4 Days CHF 2400 Submit
Journal of Imaging
jimaging
2.7 5.9 2015 18.3 Days CHF 1800 Submit
Mathematics
mathematics
2.3 4.0 2013 18.3 Days CHF 2600 Submit
Sensors
sensors
3.4 7.3 2001 18.6 Days CHF 2600 Submit

Preprints.org is a multidiscipline platform providing preprint service that is dedicated to sharing your research from the start and empowering your research journey.

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

  1. Immediately share your ideas ahead of publication and establish your research priority;
  2. Protect your idea from being stolen with this time-stamped preprint article;
  3. Enhance the exposure and impact of your research;
  4. Receive feedback from your peers in advance;
  5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (1 paper)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
15 pages, 3571 KiB  
Article
Lightweight UAV Landing Model Based on Visual Positioning
by Ning Zhang, Junnan Tan, Kaichun Yan and Sang Feng
Sensors 2025, 25(3), 884; https://doi.org/10.3390/s25030884 - 31 Jan 2025
Viewed by 409
Abstract
In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv [...] Read more.
In order to enhance the precision of UAV (unmanned aerial vehicle) landings and realize the convenient and rapid deployment of the model to the mobile terminal, this study proposes a Land-YOLO lightweight UAV-guided landing algorithm based on the YOLOv8 n model. Firstly, GhostConv replaces standard convolutions in the backbone network, leveraging existing feature maps to create additional “ghost” feature maps via low-cost linear transformations, thereby lightening the network structure. Additionally, the CSP structure of the neck network is enhanced by incorporating the PartialConv structure. This integration allows for the transmission of certain channel characteristics through identity mapping, effectively reducing both the number of parameters and the computational load of the model. Finally, the bidirectional feature pyramid network (BiFPN) module is introduced, and the accuracy and average accuracy of the model recognition landing mark are improved through the bidirectional feature fusion and weighted fusion mechanism. The experimental results show that for the landing-sign data sets collected in real and virtual environments, the Land-YOLO algorithm in this paper is 1.4% higher in precision and 0.91% higher in mAP0.5 than the original YOLOv8n baseline, which can meet the detection requirements of landing signs. The model’s memory usage and floating-point operations per second (FLOPs) have been reduced by 42.8% and 32.4%, respectively. This makes it more suitable for deployment on the mobile terminal of a UAV. Full article
Show Figures

Figure 1

Back to TopTop