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Symmetry
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Symmetry and Asymmetry Study in Object Detection
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Symmetry and Asymmetry Study in Object Detection

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 2038

Special Issue Editors

Prof. Dr. Vladimir Brtka

E-Mail Website
Guest Editor
Technical Faculty “Mihajlo Pupin” Zrenjanin, University of Novi Sad, 23000 Zrenjanin, Serbia
Interests: cyber physical systems; embedded systems; Industry 4.0
Prof. Dr. Dragan Kukolj

E-Mail Website
Guest Editor
Department of Computer Engineering, University of Novi Sad, Novi Sad, Serbia
Interests: areas of professional and research activity are multimedia systems as well as artificial intelligence techniques (neural networks; systems with fuzzy logic; expert systems) and their application in the management of complex systems; shape recognition and signal processing
Prof. Dr. Dragan M. Samardžija

E-Mail Website
Guest Editor
Nokia Bell Labs, Murray Hill, NJ 07974, USA
Interests: radio wave; propagation model; antenna
Dr. Velibor Ilic

E-Mail Website
Guest Editor
Institute for Artificial Intelligence Research and Development of Serbia, 21000 Novi Sad, Serbia
Interests: machine learning; artificial intelligence; pattern recognition; neural networks; intelligent agents; evolutionary systems; data mining; OCR; data analysis; predictive modeling; financial forecasting; algorithmic (automated) trading; HFT; interactive entertainments; image processing; 3D reconstruction; embedded systems

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the critical role of symmetry and asymmetry in object detection, exploring how these geometric properties can enhance detection algorithms. Symmetry can simplify the identification and localization of objects by reducing computational complexity and improving accuracy. Conversely, understanding asymmetry is essential for detecting irregular or occluded objects. Contributions are invited with novel methodologies, theoretical analyses, and practical applications that leverage symmetry and asymmetry in object detection. Studies may include advancements in machine learning, computer vision, and real-world applications across various domains such as robotics, autonomous systems, and medical imaging.

Prof. Dr. Vladimir Brtka
Prof. Dr. Dragan Kukolj
Prof. Dr. Dragan M. Samardžija
Dr. Velibor Ilic
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. 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

  • object detection
  • symmetry
  • asymmetry
  • computer vision
  • machine learning
  • pattern recognition
  • geometric properties
  • autonomous systems
  • robotics
  • medical imaging 

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Published Papers (5 papers)

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Research

29 pages, 31432 KiB  
Article
GAANet: Symmetry-Driven Gaussian Modeling with Additive Attention for Precise and Robust Oriented Object Detection
by Jiangang Zhu, Yi Liu, Qiang Fu and Donglin Jing
Symmetry 2025, 17(5), 653; https://doi.org/10.3390/sym17050653 (registering DOI) - 25 Apr 2025
Viewed by 114
Abstract
Oriented objects in RSI (Remote Sensing Imagery) typically present arbitrary rotations, extreme aspect ratios, multi-scale variations, and complex backgrounds. These factors often result in feature misalignment, representational ambiguity, and regression inconsistency, which significantly degrade detection performance. To address these issues, GAANet (Gaussian-Augmented Additive [...] Read more.
Oriented objects in RSI (Remote Sensing Imagery) typically present arbitrary rotations, extreme aspect ratios, multi-scale variations, and complex backgrounds. These factors often result in feature misalignment, representational ambiguity, and regression inconsistency, which significantly degrade detection performance. To address these issues, GAANet (Gaussian-Augmented Additive Network), a symmetry-driven framework for ODD (oriented object detection), is proposed. GAANet incorporates a symmetry-preserving mechanism into three critical components—feature extraction, representation modeling, and metric optimization—facilitating systematic improvements from structural representation to learning objectives. A CAX-ViT (Contextual Additive Exchange Vision Transformer) is developed to enhance multi-scale structural modeling by combining spatial–channel symmetric interactions with convolution–attention fusion. A GBBox (Gaussian Bounding Box) representation is employed, which implicitly encodes directional information through the invariance of the covariance matrix, thereby alleviating angular periodicity problems. Additionally, a GPIoU (Gaussian Product Intersection over Union) loss function is introduced to ensure geometric consistency between training objectives and the SkewIoU evaluation metric. GAANet achieved a 90.58% mAP on HRSC2016, 89.95% on UCAS-AOD, and 77.86% on the large-scale DOTA v1.0 dataset, outperforming mainstream methods across various benchmarks. In particular, GAANet showed a +3.27% mAP improvement over R3Det and a +4.68% gain over Oriented R-CNN on HRSC2016, demonstrating superior performance over representative baselines. Overall, GAANet establishes a closed-loop detection paradigm that integrates feature interaction, probabilistic modeling, and metric optimization under symmetry priors, offering both theoretical rigor and practical efficacy. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Study in Object Detection)
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16 pages, 5797 KiB  
Article
Feature Symmetry Fusion Remote Sensing Detection Network Based on Spatial Adaptive Selection
by Heng Xiao, Donglin Jing, Fujun Zhao and Shaokang Zha
Symmetry 2025, 17(4), 602; https://doi.org/10.3390/sym17040602 - 16 Apr 2025
Viewed by 219
Abstract
This paper proposes a spatially adaptive feature fine fusion network consisting of a Fast Convolution Decomposition Sequence (FCDS) and a Spatial Selection Mechanism (SSM). Firstly, in FCDS, a large kernel convolution decomposition operation is used to break down dense convolution kernels into small [...] Read more.
This paper proposes a spatially adaptive feature fine fusion network consisting of a Fast Convolution Decomposition Sequence (FCDS) and a Spatial Selection Mechanism (SSM). Firstly, in FCDS, a large kernel convolution decomposition operation is used to break down dense convolution kernels into small convolutions with gradually increasing hole rates, forming a continuous kernel sequence to obtain finer scale features. This approach significantly reduces the number of parameters, improves network inference efficiency, and preserves the spatial feature expression ability of the network. Notably, the decomposed convolution kernel sequence adopts a symmetric dilation rate increment strategy, maintaining symmetry constraints in kernel weight distribution while expanding receptive fields. On this basis, the spatial selection mechanism is utilized to enhance the key features and background differences of the target location in the feature map, dynamically allocate weights to different fine scale feature maps, and improve the adaptive ability of multi-scale domains. This mechanism employs symmetric attention weight allocation (symmetric channel attention + spatial attention) to establish complementary symmetric response patterns across feature maps in both channel and spatial dimensions. Numerous experiments have shown that our method achieves higher performance with 81.64%, 91.34%, 91.20%mAP on three commonly used remote sensing target datasets (DOTA, UCAS AOD, HRSC2016) compared to existing advanced detection networks. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Study in Object Detection)
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33 pages, 23758 KiB  
Article
Symmetry-Driven Gaussian Representation and Adaptive Assignment for Oriented Object Detection
by Jiangang Zhu, Qianjin Lin, Donglin Jing, Qiang Fu, Ting Ma and Jianming Li
Symmetry 2025, 17(4), 594; https://doi.org/10.3390/sym17040594 - 14 Apr 2025
Viewed by 180
Abstract
Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) representation, Label Assignment (LA) strategies, and regression loss functions. To address [...] Read more.
Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) representation, Label Assignment (LA) strategies, and regression loss functions. To address these limitations, this study proposes a novel detection framework, the Gaussian Detection (GaussianDet) Framework, that integrates probabilistic modeling with dynamic sample assignment to achieve more precise OD. The core design of this framework is inspired by the theory of geometric symmetry. Specifically, the radial symmetry of a two-dimensional Gaussian distribution is employed to capture the rotational and scale-invariant properties of Remote Sensing (RS) objects. By leveraging the axial symmetry of elliptical geometry, the proposed Gaussian Elliptical Intersection over Union (GEIoU) enables rotation-aligned matching, while Omni-dimensional Adaptive Assignment (ODAA) introduces dynamic symmetric constraints to optimize the spatial distribution of training samples. Specifically, a Flexible Bounding Box (FBBox) representation based on a 2D Gaussian distribution is introduced to more accurately characterize the shape, aspect ratio, and orientation of objects. In addition, the GEIoU is designed as a scale-invariant similarity metric to align regression loss with detection accuracy. To further enhance sample quality and feature learning, the ODAA strategy adaptively selects positive samples based on object scale and geometric constraints. Experimental results on the High-Resolution Ship Collection 2016 (HRSC2016) and University of Chinese Academy of Sciences–Aerial Object Detection (UCAS-AOD) datasets demonstrate that GaussianDet achieves mean Average Precision (mAP) scores of 90.53% and 96.24%, respectively. These results significantly outperform existing Oriented Object Detection (OOD) methods, thereby validating the effectiveness of the proposed approach and providing a solid theoretical foundation for future research in Remote Sensing Object Detection (RSOD). Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Study in Object Detection)
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22 pages, 17786 KiB  
Article
AFEDet: A Symmetry-Aware Deep Learning Model for Multi-Scale Object Detection in Aerial Images
by Xing Yi, Shengyu Gu, Xiaowen Wu and Donglin Jing
Symmetry 2025, 17(4), 488; https://doi.org/10.3390/sym17040488 - 24 Mar 2025
Cited by 1 | Viewed by 234
Abstract
Traditional convolutional neural networks face challenges in handling multi-scale targets in remote sensing object detection due to fixed receptive fields and simple feature fusion strategies, which affect detection accuracy. This study proposes an adaptive feature extraction object detection network (AFEDet). Compared with previous [...] Read more.
Traditional convolutional neural networks face challenges in handling multi-scale targets in remote sensing object detection due to fixed receptive fields and simple feature fusion strategies, which affect detection accuracy. This study proposes an adaptive feature extraction object detection network (AFEDet). Compared with previous models, the design philosophy of this network demonstrates greater flexibility and complementarity. First, parallel dilated convolutions effectively expand the receptive field to capture multi-scale features. Subsequently, the channel attention gating mechanism further refines these features and assigns weights based on the importance of each channel, enhancing feature quality and representation ability. Second, the multi-scale enhanced feature pyramid network (MeFPN) constructs a structurally symmetrical bidirectional transmission path. It aligns multi-scale features in the same semantic space using linear transformation, reducing scale bias and improving representation consistency. Finally, the scale adaptive loss (SAL) function dynamically adjusts loss weights according to the scale of the target, guiding the network to learn features of different scale targets evenly during training and optimizing the model’s learning direction. The proposed architecture inherently integrates symmetry principles through its bidirectional feature fusion paradigm and equilibrium-seeking mechanism. Specifically, the symmetric structure of MeFPN balances information flow between shallow and deep features, while SAL applies a symmetry-inspired loss-weighting strategy to maintain optimization consistency across different scales. Experimental results show that, on the DOTA dataset, the proposed method improves the mAP by 7.12% compared to the baseline model. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Study in Object Detection)
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26 pages, 13025 KiB  
Article
Unified Spatial-Frequency Modeling and Alignment for Multi-Scale Small Object Detection
by Jing Liu, Ying Wang, Yanyan Cao, Chaoping Guo, Peijun Shi and Pan Li
Symmetry 2025, 17(2), 242; https://doi.org/10.3390/sym17020242 - 6 Feb 2025
Viewed by 808
Abstract
Small object detection in aerial imagery remains challenging due to sparse feature representation, limited spatial resolution, and complex background interference. Current deep learning approaches enhance detection performance through multi-scale feature fusion, leveraging convolutional operations to expand the receptive field or self-attention mechanisms for [...] Read more.
Small object detection in aerial imagery remains challenging due to sparse feature representation, limited spatial resolution, and complex background interference. Current deep learning approaches enhance detection performance through multi-scale feature fusion, leveraging convolutional operations to expand the receptive field or self-attention mechanisms for global context modeling. However, these methods primarily rely on spatial-domain features, while self-attention introduces high computational costs, and conventional fusion strategies (e.g., concatenation or addition) often result in weak feature correlation or boundary misalignment. To address these challenges, we propose a unified spatial-frequency modeling and multi-scale alignment fusion framework, termed USF-DETR, for small object detection. The framework comprises three key modules: the Spatial-Frequency Interaction Backbone (SFIB), the Dual Alignment and Balance Fusion FPN (DABF-FPN), and the Efficient Attention-AIFI (EA-AIFI). The SFIB integrates the Scharr operator for spatial edge and detail extraction and FFT/IFFT for capturing frequency-domain patterns, achieving a balanced fusion of global semantics and local details. The DABF-FPN employs bidirectional geometric alignment and adaptive attention to enhance the significance expression of the target area, suppress background noise, and improve feature asymmetry across scales. The EA-AIFI streamlines the Transformer attention mechanism by removing key-value interactions and encoding query relationships via linear projections, significantly boosting inference speed and contextual modeling. Experiments on the VisDrone and TinyPerson datasets demonstrate the effectiveness of USF-DETR, achieving improvements of 2.3% and 1.4% mAP over baselines, respectively, while balancing accuracy and computational efficiency. The framework outperforms state-of-the-art methods in small object detection. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry Study in Object Detection)
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