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Symmetry
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Deep Learning and Deep Learning Synergy of Transformers and Symmetry...
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Deep Learning and Deep Learning Synergy of Transformers and Symmetry in Small Object Detection and Tracking

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 31 May 2026 | Viewed by 5932

Special Issue Editors

Dr. Fengping An

E-Mail Website
Guest Editor
School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China
Interests: image processing; artificial intelligence;deep learning; target detection;pattern recognition; target recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Haitao Xu

E-Mail Website
Guest Editor
Department of Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: wireless resource allocation and management; wireless communications and networking; dynamic game and mean field game theory; big data analysis; security
Special Issues, Collections and Topics in MDPI journals
Dr. Chuyang Ye

E-Mail Website
Guest Editor Assistant
School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing 100811, China
Interests: medical image processing; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Small-object detection and tracking is one of the most challenging and fundamental topics in computer vision. These objects are typically characterized by their small size, low contrast, and blurred edges, which collectively complicate the tasks of detection and tracking. In recent years, advancements in this field have facilitated the application of small-object detection and tracking in remote sensing imagery across diverse domains, including mineral exploration, precision agriculture, urban planning, forestry management, military target identification, and disaster assessment. Despite these advancements, several critical challenges persist in real-world applications. Key issues include the difficulty of extracting detailed information from small objects, the trade-off between detection accuracy and computational efficiency, the ability to identify unknown or untrained categories within image data, and the effective tracking of small objects over time. Addressing these challenges is essential for advancing the practical utility of small-object detection and tracking systems.

Therefore, we invite submissions of papers on theoretical research and practical applications related to the deep learning and deep learning synergy of transformers and symmetry architecture for small-object detection related to image processing.

Prof. Dr. Fengping An
Prof. Dr. Haitao Xu
Guest Editors

Dr. Chuyang Ye
Guest Editor Assistant

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 250 words) can be sent to the Editorial Office for assessment.

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. Symmetry is an international peer-reviewed open access monthly 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 2400 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

  • deep learning
  • image processing
  • object recognition
  • artificial intelligence

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Published Papers (2 papers)

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Research

27 pages, 1255 KB  
Article
CMTA: Infrared Detection Model for Power Facility Components via Multi-Angle Perception and Transattn Fusion
by Zhongyuan Fan, Lufeng Yuan, Biyao Wen, Qiang Liu and Gengkun Wu
Symmetry 2025, 17(11), 1909; https://doi.org/10.3390/sym17111909 - 7 Nov 2025
Viewed by 361
Abstract
Infrared detection of defects in power facilities is critical to the safe operation and fault early warning of power grids. However, conventional inspection methods have distinct limitations, such as delayed response and insufficient condition visualization. To address the pain points and technical challenges [...] Read more.
Infrared detection of defects in power facilities is critical to the safe operation and fault early warning of power grids. However, conventional inspection methods have distinct limitations, such as delayed response and insufficient condition visualization. To address the pain points and technical challenges of the aforementioned inspection modes, this study proposes a deep learning network model based on multi-angle perception and Transattn feature fusion. This model can effectively improve the defect recognition ability of power facility components in complex scenarios. Firstly, a modified MAPC module is introduced, which enhances the extraction of edge contours of power facility components and detailed infrared thermal textures. Secondly, an innovative Transattn module is proposed to dynamically focus on the core component regions of power facilities. Finally, a feature fusion strategy is used to efficiently integrate the feature maps from each module, outputting component localization results and defect category information. Experimental results based on the infrared detection dataset of power facility components show that compared with classical detection models such as YOLOv10 and DDN, the proposed CMTA model achieves the best performance in all indicators: the highest mAP50 reaches 85.01%, the frame rate (FPS) is 252 frames per second, the parameter count is only 2.8 M, and it significantly shortens the fault response time of operation and maintenance personnel. Full article
(This article belongs to the Special Issue Deep Learning and Deep Learning Synergy of Transformers and Symmetry in Small Object Detection and Tracking)
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18 pages, 5013 KB  
Article
Enhancing Document Forgery Detection with Edge-Focused Deep Learning
by Yong-Yeol Bae, Dae-Jea Cho and Ki-Hyun Jung
Symmetry 2025, 17(8), 1208; https://doi.org/10.3390/sym17081208 - 30 Jul 2025
Viewed by 5108
Abstract
Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically [...] Read more.
Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article
(This article belongs to the Special Issue Deep Learning and Deep Learning Synergy of Transformers and Symmetry in Small Object Detection and Tracking)
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